Thèses : « Bayesian belief »

1

Suermondt, Henri Jacques. « Explanation in Bayesian belief networks ». Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.

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Wilson, Simon Trevor. « Applications of cyclic belief propagation ». Thesis, University of Cambridge, 2000. https://www.repository.cam.ac.uk/handle/1810/251732.

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Olson, John Thomas. « Hardware/software partitioning utilizing Bayesian belief networks ». Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284156.

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In heterogeneous systems design, partitioning of the functional specifications into hardware and software components is an important procedure. Often, a hardware platform is chosen and the software is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented here is novel in that it uses Bayesian Belief Networks (BBNs) to categorize functional components into hardware and software classifications. The BBN's ability to propagate evidence permits the effects of a classification decision made about one function to be felt throughout the entire network. In addition, because BBNs have a belief of hypotheses as their core, a quantitative measurement as to the correctness of a partitioning decision is achieved. In this research, the motivation and background material are presented first. Next, a methodology for automatically generating the qualitative, structural portion of BBN, and the quantitative link matrices is given. Lastly, a case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, called model-based Codesign.

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Hild, Matthias. « Induction and the dynamics of belief ». Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389702.

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Heather, Adele. « Bayesian belief networks using conditional phase-type distributions ». Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369955.

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Likhari, Amitoj S. « Computing a maximal clique using Bayesian belief networks ». [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000735.

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Pershad, Rinku. « A Bayesian belief network for corporate credit risk assessment ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0022/MQ50360.pdf.

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Gaskell, Alexander Paul. « Sensor managememt in mobile robotics using Bayesian belief networks ». Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282200.

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Lampis, Mariapia. « Application of Bayesian Belief Networks to system fault diagnostics ». Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6864.

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Fault diagnostic methods aim to recognize when a fault exists on a system and to identify the failures which have caused it. The fault symptoms are obtained from readings of sensors located on the system. When the observed readings do not match those expected then a fault can exist. Using the detailed information provided by the sensors a list of the failures that are potential causes of the symptoms can be deduced. In the last decades, fault diagnostics has received growing attention due to the complexity of modern systems and the consequent need of more sophisticated techniques to identify failures when they occur. Detecting the causes of a fault quickly and efficiently means reducing the costs associated with the system unavailability and, in certain cases, avoiding the risks of unsafe operating conditions. Bayesian Belief Networks (BBNs) are probabilistic graphical models that were developed for artificial intelligence applications but are now applied in many fields. They are ideal for modelling the causal relations between faults and symptoms used in fault diagnostic processes. The probabilities of events within the BBN can be updated following observations (evidence) about the system state. In this thesis it is investigated how BBNs can be applied to the diagnosis of faults on a system with a model-based approach. Initially Fault Trees (FTs) are constructed to indicate how the component failures can combine to cause unexpected deviations in the variables monitored by the sensors. The FTs are then converted into BBNs and these are combined in one network that represents the system. The posterior probabilities of the component failures give a measure of which components have caused the symptoms observed. The technique is able to handle dynamics in the system introducing dynamic patterns for the sensor readings in the logic structure of the BBNs. The method is applied to two systems: a simple water tank system and a more complex fuel rig system. The results from the two applications are validated using two simulation codes in C++ by which the system faulty states are obtained together with the failures that cause them. The accuracy of the BBN results is evaluated by comparing the actual causes found with the simulation with the potential causes obtained with the diagnostic method.

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Carriger, John Fletcher Jr. « Bayesian belief networks for decision analysis in environmental management ». W&M ScholarWorks, 2009. https://scholarworks.wm.edu/etd/1539791560.

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In decision problems that rely on technical or scientific data, values are often not explicitly considered, resulting in suboptimal environmental management decision-making. Yet, valuation is an integral part of the overall environmental management process. An environmental decision-making framework that places valuation at the forefront of the process is advocated. The application of values to environmental decisions should occur at every phase of analysis, not just the final weighing of decisions. Value-focused thinking will be used here to structure the problem and determine what is important. Management tasks, environmental or otherwise, cannot rely solely on objective criteria. Stakeholder input and values, and regulatory guidelines are normally considered along with relevant monitoring and modeling data output. Though formal risk management normally contains many decision tools, a unified procedure should exist to weigh evidence as well as formally integrate opinion and observation. A decision framework should be a helpful tool to bring together lines of evidence and values necessary to make important and costly decisions. If the decision-making consequences are detrimental, others can understand why a decision was made if a rationale is available. The best way to understand how a decision was made is to present the decision process from a value-focused perspective. Understanding the difference between objectives, alternatives, and criteria in a decision problem and placing value on features of interests should improve current informal environmental management decisions immensely. Though the current work will not explicitly evaluate costs and benefits, an approach that uses Bayesian Belief Networks (BBNs) and influence diagrams (IDs) is proposed. From the value-focused decision analysis, IDs will be created to weigh the evidence of the various alternative actions needed to reach items of value. An ID can be constructed once the major objectives, alternatives, and criteria are identified. The ID construction phase arranges the information determined in the decision analysis so that experts and lay people can evaluate what is important in a problem and how decisions and other factors influence it. Constructing an ID would include mapping the causal factors and decisions in a directed acyclic graph while preserving assumptions of conditional independence. The first three chapters of this thesis synthesized information from the decision analysis literature to establish an approach that will be beneficial to environmental management. The final two chapters developed examples of the approach that applies Bayesian decision networks in environmental management. Two topics in the final chapters were used to illustrate the framework's potential effectiveness: pesticide ecological risk assessment and natural resource management of Chesapeake Bay seagrass. The pesticide risk management scheme incorporated risk assessment evidence from various models to balance ecological risk management with spraying efficacy judgments. The seagrass assessment evaluated the ability of a BBN to assimilate water quality monitoring data in decision-making that reflect remedial goals. Assessing outcomes and the influences of future processes on restoration targets can be accomplished within the framework of a formal decision analysis with Bayesian networks.

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Sahely, Brian S. G. E. « Development of a Bayesian belief network for anaerobic wastewater treatment ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0027/MQ50490.pdf.

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Wilson, Kevin James. « Belief representation for counts in Bayesian inference and experimental design ». Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1217.

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Bayesian inference for such things as collections of related binomial or Poisson distributions typically involves rather indirect prior specifications and intensive numerical methods (usually Markov chain Monte Carlo) for posterior evaluations. As well as requiring some rather unnatural prior judgements this creates practical difficulties in problems such as experimental design. This thesis investigates some possible alternative approaches to this problem with the aims of making prior specification more feasible and making the calculations necessary for updating beliefs or for designing experiments less demanding, while maintaining coherence. Both fully Bayesian and Bayes linear approaches are considered initially. The most promising utilises Bayes linear kinematics in which simple conjugate specifications for individual counts are linked through a Bayes linear belief structure. Intensive numerical methods are not required. The use of transformations of the binomial and Poisson parameters is proposed. The approach is illustrated in two examples from reliability analysis, one involving Poisson counts of failures, the other involving binomial counts in an analysis of failure times. A survival example based on a piecewise constant hazards model is also investigated. Applying this approach to the design of experiments greatly reduces the computational burden when compared to standard fully Bayesian approaches and the problem can be solved without the need for intensive numerical methods. The method is illustrated using two examples, one based on usability testing and the other on bioassay.

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Rababah, Osama. « Quality assessment of e-commerce websites using Bayesian belief networks ». Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/8011.

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This thesis raises the issue of quality in E-commerce websites and addresses methodologies and approaches to standardize their assessment. The thesis blends the knowledge of academic research with the opinions and insights from experts and practitioners in the field to provide a comprehensive view of the issues and their remedies. The most experienced and successful E-commerce companies are beginning to realize that key determinants of success or failure are not merely a web presence or a low price but delivering a high quality website. Recent research shows that price and promotion are no longer the main draws for customers to make a decision on a purchase. More sophisticated online customers would rather pay a higher price to a provider with high quality service. Given that the establishment of an E-commerce website is mainly a software development effort; there are several standards that apply in governing the quality of such development. There seems to be an almost overwhelming abundance of quality standards that lead to a high level of cynicism and skepticism surrounding them and the eventual lack of use. Furthermore, no standard can directly predict the quality a website under development is going to achieve. Past approaches concerning the quality of E-commerce websites emphasize usability standards using techniques like feature inspection and collecting data about end-users' opinion by questionnaires. These methods provide important feedback and their results can be utilized as a useful background for future work, however, they do not contribute directly to a dynamic model that enables forecasting. The study of quality in the domain of the Internet in general, and E-commerce in particular, poses new challenges as technology evolves, including methods and metrics for estimating, managing quality during the product life cycle and quality-of-use measurement. The solution proposed by this research is to use a Bayesian Belief Network model. This model provides a consistent and practical approach to assessing the quality of the website. The assessment can be carried out before the completion of the website development, thus, providing insight on the trend and direction for correction and improvements. It can also be carried out on completed and operational work, providing analysis of areas for improvement. The model should be relatively quick and practical in providing an overall comprehensive assessment with root-cause analysis that would lead to corrective measures to improve the quality of the E-commerce website. In this research idioms were applied in realizing a complete Bayesian Belief Network model. The conclusions are measured against comparative assessment to validate the practical benefits of the work accomplished. The WebQual method was utilized to validate the "belief" established by the model.

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Beaver, Justin. « A LIFE CYCLE SOFTWARE QUALITY MODEL USING BAYESIAN BELIEF NETWORKS ». Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2353.

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Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering

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Ang, Kwang Chien. « Applying Bayesian belief Networks in Sun Tzu's Art of Wa / ». Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FAng.pdf.

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Ang, Kwang Chien. « Applying Bayesian belief networks in Sun Tzu's Art of war ». Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1323.

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Approved for public release; distribution in unlimited.
The principles of Sun Tzu's Art of War have been widely used by business executives and military officers with much success in the realm of competition and conflict. However, when conflict situations arise in a highly stressful environment coupled with the pressure of time, decision makers may not be able to consider all the key concepts when forming their decisions or strategies. Therefore, a structured reasoning approach may be used to apply Sun Tzu's principles correctly and fully. Sun Tzu's principles are believed to be able to be modeled mathematically; hence, a Bayesian Network model (a form of mathematical tool using probability theory) is used to capture Sun Tzu's principles and provide the structured reasoning approach. Scholars have identified incompleteness in Sun Tzu's appreciation of information in war and his application of secret agents. This incompleteness resulted in circular reasoning when both sides of the conflict apply his principles. This circular reasoning can be resolved through the use of advanced probability theory. A Bayesian Network Model however, not only provides a structured reasoning approach, but more importantly, it can also resolve the circular reasoning problem that has been identified.
Captain, Singapore Army

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HUSSAIN, KAMALY MAHBUB. « Bayesian belief networks for guidedremote diagnostics and troubleshootingof heavy vehicles ». Thesis, KTH, Maskinkonstruktion (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209906.

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Kostnadreducering och eektivisering av reparationer (t.ex i bilindustrin) har varit malet for forskningen kring guidad diagnostik i snart tvadecennier [ 1], med en onskan till intuitiv felsokning och reparation utan tidigare expert kunskaper. Detta betyder att automation vid diagnostik har blivit en nodvandighet dar det ar mojligt att forstakomplexa system samtidigt som operatoren ges tillrackligt med stod och expertkunskaper fr att kunna tillfora kompetent assistans. Detta examensarbete som utfordes paScania CV AB undersoker hur ett sadant system skulle utformas och prestera samtidigt som arbetet ligger till grund for vidare utveckling av guidad fjarrdiagnostik hos Scania. Resultatet kommer att behandla tre analysomraden. Ett, dem observationer fran fordonet som ar indikationer om ett felaktigt system. Tva, anvandning av ett Basianskt natverk for att gora en diagnos pasystemet samt undersoka hurvida tillvagagangasattet ar eektivt eller inte for den intiutiva kanslan. Tre, en studie och implementation av en eektiv felsokningsalgoritm som minimerar reparationskostnaden baserad paden givna diagnosen, kostnad for reparationav komponenter samt reparationstiden. Examensarbetet kommer forst att presenteras med en djupgaende teoridel och foljs av implementation av teorin till en funktionell prototyp.
Intuitive troubleshooting and fault repair without the need of prior expert knowledge of automobiles has become essential in an aim for cost-minimization and eectiveness of repairs, it has been a focus in troubleshooting research for the past decade or two[ 1]. This calls for an automated diagnosis system that is simple to understand and operate whilst at the same time provides the operator with the expert knowledge required for competent assistance. Thismaster thesis conducted at Scania CV AB will investigate how such a system would function and perform, providing a ground work for further development. The result will incorporate three aspects of analysis. First, the observations from the vehicle indicating that something is wrong or faulty. Second, the use of a Bayesian network, a model structure that describes probabilistic relationships and dependencies among system variables, for diagnostic purposes and to examine its haul on intuitive understanding of the system faults. Third, an implementation and study of a troubleshooting algorithm that will minimize the cost of repair based on an easy calculated metric that takes into consideration the probability of fault, cost of observation and the cost of repair (and indirectly also the mean repair time). Given a particular diagnosis, an optimized action plan and repair sequence is given. A thorough review of the underlying theory will be provided for the reader in the rst part of the report, where a slight deviation will be made to further investigate the use of Bayesian lters and its eect on the a priori probabilities used in the Bayesian model. In the nal part the reader will be guided through the implementation of the given theory and emersion of a working prototype.

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Ejaz, Azad. « Using a Bayesian Belief Network for Going-Concern Risk Evaluation ». NSUWorks, 2005. http://nsuworks.nova.edu/gscis_etd/500.

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An auditor's verdict on client's financial health is delivered in the form of a going concern (GC) opinion. Although an auditor is not required to predict the financial future of a client, stakeholders take the GC opinion as a guideline on a company's financial health. The GC opinion has been a subject of much debate in the financial literature, as it is one of the most widely read parts of an audit report. Researchers and academicians believe that auditors have made costly mistakes in rendering GC opinions. Several factors have been identified as the root causes for these mistakes, including growing business complexities, insufficient auditor training, internal and external pressures, personal biases, economic considerations, and fear of litigation. To overcome these difficulties, researchers have been trying to devise effective audit tools to help auditors form accurate GC opinions on clients ' financial future. Introduction of ratio-based bankruptcy models using a variety of statistical techniques are attempts in the right direction. The results of such efforts, though not perfect, are encouraging. This study examined several popular ratio-based statistical models and their weaknesses and limitations. The author suggests a new model based on the robust Bayesian Belief Network (BBN) technique. Based on sound Bayesian theory, this model provides remedies against the reported deficiencies of the ratio-based techniques. The proposed system, instead of comparing a company's financial ratios with the industrywide ratios, measures the internal financial changes within a company during a particular year and uses the changing financial pattern to predict the financial viability of the company. Unlike other popular models, the proposed model takes various qualitative factors into consideration before delivering the GC verdict. The proposed system is verified and validated by comparing its results with the industry de facto Z-score model.

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Siraj, Tammeen. « Seismic risk assessment of high-voltage transformers using Bayesian belief networks ». Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44245.

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Past earthquake records showed that a large magnitude earthquake can cause severe damage to high-voltage substations, which may lead to power disruption for a significant amount of time. A high-voltage transformer is one of the key components of a substation. This thesis proposes a probabilistic framework using Bayesian belief network (BBN) model to predict the vulnerability of a high-voltage transformer for a seismic event. BBN has many capabilities that make it well suited for the proposed risk assessment method. This thesis considers past studies, expert knowledge and reported causes of failures to develop an initial integrated risk assessment framework that acknowledges multiple failure modes. Therefore, the framework incorporates major causes of transformer vulnerability due to seismicity, such as liquefaction, rocking response of transformer, or interaction between interconnected equipment. To demonstrate the application of this framework, this thesis elaborates each step of the framework. Finally, the sensitivity analysis was carried out to evaluate the effects of input variables on transformer damage. The paper also illustrates two predictive models using response surface method (RSM) and Markov chain. The proposed framework is particularly handy to perform, and the results can be useful to support decisions on mitigation measures and seismic risk prediction.

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Luo, Zhiyuan. « A probabilistic reasoning and learning system based on Bayesian belief networks ». Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1490.

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Leerojanaprapa, Kanogkan. « A Bayesian belief network modelling process for systemic supply chain risk ». Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23564.

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To effectively manage risk in supply chains, it is important to understand the interrelationships between risk events that might affect the flow of material, products and information within the chain. Typical supply chain risk management tends to treat events as if they are independent and so fail to capture the systemic nature of supply chain risks. This thesis addresses this shortcoming by developing a quantitative modelling process to support systemic supply chain risk analysis. Bayesian Belief Network (BBN) models are able to capture both the aleatory and epistemic uncertainties associated with supply chains and to represent probabilistic dependency relationships. A visual modelling process, grounded in the theory of BBN and the decision context of supply chain risk management, is developed to capture the knowledge and probability judgements of relevant stakeholders. An experiment has been conducted to evaluate alternative approaches to structuring a BBN model for supply risk. It is found that building causal maps provides a good basis for translating stakeholder cause-effect knowledge about the supply chain risks into a formal graphical probability model, which underpins the BBN. The modelling process has been evaluated through a longitudinal case for the hospital medicine supply of NHS Greater Glasgow & Clyde. A BBN model has been developed in collaboration with relevant stakeholders who have expertise in all or part of the medicine supply chain. The perceptions of these stakeholders about the modelling process and results generated have been formally gathered and analysed. The BBN model of the medicine supply chain has provided insight into risks not captured by conventional risk management methods and supported deeper understanding of risk through exploration of modelling scenarios. Analysis of stakeholder evaluation of the modelling process provided valuable insights into the operationalization of BBN modelling for supply risk and has informed the final modelling process developed through this research.

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Goodson, Justin. « Assessing the quality of care in nursing homes through Bayesian belief networks ». Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4286.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 13, 2006) Includes bibliographical references.

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Gilson, Robert. « Minimizing input acquisition costs in a Bayesian belief network-based expert system / ». Thesis, Connect to this title online ; UW restricted, 1997. http://hdl.handle.net/1773/8763.

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Lee, Keen Sing 1972. « Quantifying the Main Battle Tank's architectural trade space using Bayesian Belief Network ». Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/34733.

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Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 2004.
Includes bibliographical references (p. 239-240).
The design and development of a Main Battle Tank can be characterized as a technically challenging and organizationally complex project. These projects are driven not only by the essential engineering and logistic tasks; as the frequency of technological innovation increases system architects are motivated to apply an effective method to assess the risks and benefits of adopting technological alternatives. This thesis applies Bayesian Belief Network as a quantitative modeling and metrics calculation framework in establishing the preference order of possible architectural choices during the development of a Main Battle Tank. A framework of metrics was developed for the architect to communicate objectively with stakeholders and respond to challenges raised. These inputs were then encoded as variables in a global Bayesian Belief Network. Using a change propagation algorithm any changes in the probabilities of individual variables would trigger changes throughout the entire network and can be used as informing messages to the stakeholders to reflect the consequences of these changes. Two Bayesian Belief Networks were developed and tested to understand the effectiveness and sensitivities to the variables. The successful development of the Bayesian Belief Network offers technical and organizational benefits to the system architect. From the technical viewpoint, the model benefits include performing system tradeoff studies, iterating the design to incorporate feedback quickly, analyzing the sensitivity and impact of each design change to the overall system, and identifying critical areas to allocate resources. From an organizational process perspective, it enables speedier knowledge transfer in the project, and enables the engineers
(cont.) to be knowledgeable about how their localized change could affect other sub-systems.
by Keen Sing Lee.
S.M.

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Chong, H. G. « Predicting and diagnosing faults in wastewater treatment process by Bayesian Belief Networks ». Thesis, Aston University, 1997. http://publications.aston.ac.uk/14154/.

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Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.

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Nunoo, Samuel. « Bayesian Belief network approach to slope management in British Columbia open pits ». Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/57946.

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The stability of rock slopes is a major safety issue in open pit mining. It is important for rock engineers and mine operators to be knowledgeable about their pit wall behaviour, and, more specifically, to recognize appropriate conditions that trigger the need to issue warnings or stop work orders. With the current increase in the number of open pit mines in British Columbia and the deepening of existing pits, there is a need for rational, scientifically based decisions in response to measured pit wall performance. The main objective of this research was to develop and establish a Bayesian Belief Network (BBN) model and outline appropriate operational responses to manage slopes in large open pit porphyry mines. The BBN model can be tailored to specific geotechnical conditions and pit wall configurations. The research integrated available geotechnical engineering data and knowledge, including expert knowledge, ground water conditions, slope geometry, mining activity (blast damage), and consequences of failure, into one platform that can establish appropriate operational responses. A range of pre-defined actions ranging from normal pit operations to orders to stop work and evacuate the pit were defined in this research as operational responses or pit management decisions. These operational responses were linked in the BBN model to predicted states of pit wall movement and estimates of the consequences of these movements. A new relationship was proposed to estimate the travel distance from a wide range of pit slope failure debris volumes. The relationship accounts for a potential rockslide transforming into a rock avalanche. The BBN model was used to retroactively predict the appropriate operational response at four mines to using data from past slope instabilities. The results indicate that equipment damage as well as production losses could have been minimized or prevented had the BBN model been used by the mine operators at the time of each slope instability. The methodology described in the thesis provides the foundation for an innovative tool for the selection of appropriate operational responses linked to measured slope velocity, potential rockslide debris volume, and potential travel distance of the debris.
Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate

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Masood, Adnan. « Measuring Interestingness in Outliers with Explanation Facility using Belief Networks ». NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/232.

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This research explores the potential of improving the explainability of outliers using Bayesian Belief Networks as background knowledge. Outliers are deviations from the usual trends of data. Mining outliers may help discover potential anomalies and fraudulent activities. Meaningful outliers can be retrieved and analyzed by using domain knowledge. Domain knowledge (or background knowledge) is represented using probabilistic graphical models such as Bayesian belief networks. Bayesian networks are graph-based representation used to model and encode mutual relationships between entities. Due to their probabilistic graphical nature, Belief Networks are an ideal way to capture the sensitivity, causal inference, uncertainty and background knowledge in real world data sets. Bayesian Networks effectively present the causal relationships between different entities (nodes) using conditional probability. This probabilistic relationship shows the degree of belief between entities. A quantitative measure which computes changes in this degree of belief acts as a sensitivity measure . The first contribution of this research is enhancing the performance for measurement of sensitivity based on earlier research work, the Interestingness Filtering Engine Miner algorithm. The algorithm developed (IBOX - Interestingness based Bayesian outlier eXplainer) provides progressive improvement in the performance and sensitivity scoring of earlier works. Earlier approaches compute sensitivity by measuring divergence among conditional probability of training and test data, while using only couple of probabilistic interestingness measures such as Mutual information and Support to calculate belief sensitivity. With ingrained support from the literature as well as quantitative evidence, IBOX provides a framework to use multiple interestingness measures resulting in better performance and improved sensitivity analysis. The results provide improved performance, and therefore explainability of rare class entities. This research quantitatively validated probabilistic interestingness measures as an effective sensitivity analysis technique in rare class mining. This results in a novel, original, and progressive research contribution to the areas of probabilistic graphical models and outlier analysis.

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Rao, Justin M. « Essays in belief formation and decision making ». Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397060.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed March 29, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 98-102).

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Durá-Bernal, Salvador. « A cortical model of object perception based on Bayesian networks and belief propagation ». Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/540.

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Evidence suggests that high-level feedback plays an important role in visual perception by shaping the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier 2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al. 2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is driven by feedback connections. Generative models and Bayesian belief propagation have been suggested to provide a theoretical framework that can account for feedback connectivity, explain psychophysical and physiological results, and map well onto the hierarchical distributed cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996, Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009, Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009). The present study explores the role of feedback in object perception, taking as a starting point the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity. A Bayesian network that captures the structure and properties of the HMAX model is developed, replacing the classical deterministic view with a probabilistic interpretation. The proposed model approximates the selectivity and invariance operations of the HMAX model using the belief propagation algorithm. Hence, the model not only achieves successful feedforward recognition invariant to position and size, but is also able to reproduce modulatory effects of higher-level feedback, such as illusory contour completion, attention and mental imagery. Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart probabilistic approaches and supported by current experimental evidence, of the interaction between top-down global feedback and bottom-up local evidence in the context of hierarchical object perception.

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Das, Bibhash. « Technical Due Diligence Assessment and Bayesian Belief Networks Methodology for Wind Power Projects ». Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-224063.

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A Technical Due Diligence (TDD) investigation is an important step in the process of obtaining financing, or in mergers and acquisitions, for a wind power project. The investigation, the scope of which varies depending on the stage and nature of the project, involves reviewing important documentation relating to different aspects of the project, assessing potential risks in terms of the quality of the information available and suggesting mitigation or other risk management measures where required. A TDD assessment can greatly benefit from increased objectivity in terms of the reviewed aspects as it enables a sharper focus on the important risk elements and also provides a better appreciation of the investigated parameters. This master’s thesis has been an attempt to introduce more objectivity in the technical due diligence process followed at the host company. Thereafter, a points-based scoring system was devised to quantify the answered questions. The different aspects under investigation have a complex interrelationship and the resulting risks can be viewed as an outcome of a causal framework. To identify this causal framework the concept of Bayesian Belief Networks has been assessed. The resulting Bayesian Networks can be considered to provide a holistic framework for risk analysis within the TDD assessment process. The importance of accurate analysis of likelihood information for accurate analysis of Bayesian analysis has been identified. The statistical data set for the right framework needs to be generated to have the right correct setting for Bayesian analysis in the future studies. The objectiveness of the TDD process can be further enhanced by taking into consideration the capability of the investing body to handle the identified risks and also benchmarking risky aspects with industry standards or historical precedence.

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Kevorkian, Christopher George. « UAS Risk Analysis using Bayesian Belief Networks : An Application to the VirginiaTech ESPAARO ». Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73047.

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Small Unmanned Aerial Vehicles (SUAVs) are rapidly being adopted in the National Airspace (NAS) but experience a much higher failure rate than traditional aircraft. These SUAVs are quickly becoming complex enough to investigate alternative methods of failure analysis. This thesis proposes a method of expanding on the Fault Tree Analysis (FTA) method to a Bayesian Belief Network (BBN) model. FTA is demonstrated to be a special case of BBN and BBN can allow for more complex interactions between nodes than is allowed by FTA. A model can be investigated to determine the components to which failure is most sensitive and allow for redundancies or mitigations against those failures. The introduced method is then applied to the Virginia Tech ESPAARO SUAV.
Master of Science

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Luo, Wuben. « A comparative assessment of Dempster-Shafer and Bayesian belief in civil engineering applications ». Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28500.

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The Bayesian theory has long been the predominate method in dealing with uncertainties in civil engineering practice including water resources engineering. However, it imposes unnecessary restrictive requirements on inferential problems. Concerns thus arise about the effectiveness of using Bayesian theory in dealing with more general inferential problems. The recently developed Dempster-Shafer theory appears to be able to surmount the limitations of Bayesian theory. The new theory was originally proposed as a pure mathematical theory. A reasonable amount of work has been done in trying to adopt this new theory in practice, most of this work being related to inexact inference in expert systems and all of the work still remaining in the fundamental stage. The purpose of this research is first to compare the two theories and second to try to apply Dempster-Shafer theory in solving real problems in water resources engineering. In comparing Bayesian and Dempster-Shafer theory, the equivalent situation between these two theories under a special situation is discussed first. The divergence of results from Dempster-Shafer and Bayesian approaches under more general situations where Bayesian theory is unsatisfactory is then examined. Following this, the conceptual difference between the two theories is argued. Also discussed in the first part of this research is the issue of dealing with evidence including classifying sources of evidence and expressing them through belief functions. In attempting to adopt Dempster-Shafer theory in engineering practice, the Dempster-Shafer decision theory, i.e. the application of Dempster-Shafer theory within the framework of conventional decision theory, is introduced. The application of this new decision theory is demonstrated through a water resources engineering design example.
Applied Science, Faculty of
Civil Engineering, Department of
Graduate

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Webb, Stephen Scott Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. « Belief driven autonomous manipulator pose selection for less controlled environments ». Publisher:University of New South Wales. Mechanical & ; Manufacturing Engineering, 2008. http://handle.unsw.edu.au/1959.4/43090.

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This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.

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Agrawal, Prerna. « Tool And Algorithms for Rapid Source Term Prediction (RASTEP) Based on Bayesian Belief Networks ». Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256964.

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In case of an accident in a nuclear power plant (NPP), the fast and cor-rect identification of the NPP state that would give a prediction of a possible radioactive release presents a major challenge to both nuclear power plants and regulators. Such prediction is important so that correct and timely decisions and measures are taken to mitigate accident consequences, such as evacuation of people from areas around the power plant. Recent research work [2][3] proposes analyzing the NPP using the Bayesian Belief Network models as a solution to this problem. A BBN is a graphical model that represents any entity with a set of connected nodes. These nodes represent the random variables and the connections between the nodes represent the conditional dependencies between them [7]. However, the BBN models alone are not suitable for use in off-site locations under high stress conditions by people who are not experts. Hence there arises a need for an interface that would – - Be easy to operate by non-experts under high stress situations with incomplete knowledge of the plant state. - Provide the more detailed information about the network that is not easy for users to read out from the BBN itself. - Provide good graphical displays of the radioactive release predictions and other statistics of the network. One such tool is developed as a part of this master thesis project. The contribution is twofold – - Analyzing the user requirements, designing the architecture and development of the tool. - Design and implementation of the algorithms for extracting additional information from the network which is not easy to read out while working directly with the BBN. This kind of information helps the user to take some decisions with entering the observations when the user is not a BBN expert. For instance, it helps the user to know which nodes are important to answer and which nodes can be left out. This also helps the user to interpret the intermediate state of the BBN model of the plant. The tool and the algorithms were evaluated by an expert user in order to assess them based on ease of use, value of the analysis output and the processing time. This project work was carried forward in collaboration with Swedish Radiation Safety Authority (SSM) [8]. SSM is already assessing the tool with the goal to obtain fast and independent predictions of radioactive releases based on plant observations.

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Kim, Dohyoung 1970. « Bayesian Belief Network (BBN)-based advisory system development for steam generator replacement project management ». Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/30011.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 2002.
Includes bibliographical references (leaves 192-194).
The growing need for improved project management technique points to the usefulness of a knowledge-base advisory system to help project managers understand current and future project status and optimize decisions based upon the project performances. The work here demonstrates the framework of an advisory system with improved ability in project management. Based upon the literature survey and discussion with relevant experts, the Bayesian Belief Network (BBN) approach was selected to model the steam generator replacement proj ect management problem, where the situation holds inherently large uncertainty and complexities, since it has a superior ability to treat complexities, uncertainty management, systematic decision making, inference mechanism, knowledge representation and model modification for newly acquired knowledge. Two modes of advisory system have been constructed. As the first mode, the predictive mode has been developed, which can predict future project performance state probability distributions, assuming no intervening management action. The second mode is the advisory mode, which can identify the optimal action among alternatives based upon the expected net benefit values that are incorporating two important components: 1) expected immediate net benefits at post-action time, and 2) the expected long term benefit (or penalty) at scheduled project completion time. During the work, new indices for important variables have been newly developed for effective and efficient project status monitoring. With application of developed indices to the advisory system, the long term benefit (or penalty) found to be the most important factor in determining the optimal action by the project management during the decision
(cont.) making process and was confirmed by the domain experts. As a result, the effort has been focused on incorporating the long term benefit (or penalty) concept in order to provide more reliable and accurate advice to the project managers. In addition, in order to facilitate the communication between the BBN models and the users, an interface program has been developed using the Visual Basic language.
by Dohyoung Kim.
Sc.D.

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REN, Qing. « Applying Bayesian Belief Network To Understand Public Perception On Green Stormwater Infrastructures In Vermont ». ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/835.

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Decisions of adopting best management practices made on residential properties play an important role in reduction of nutrient loading from non-point sources into Lake Champlain and other waterbodies in Vermont. In this study, we use Bayesian belief network (BBN) to analyze a 2015 survey dataset about adoption of six types of green infrastructures (GSIs) in Vermont’s residential areas. Learning BBNs from physical probabilities of the variables provides a visually explicit approach to reveal the message delivered by the dataset. Using both unsupervised and supervised machine learning algorithms, we are able to generate networks that connect the variables of interest and conduct inference to look into the probabilistic associations between the variables. Unsupervised learning reveals the underlying structures of the dataset without presumptions. Supervised learning provides insights for how each factor (e.g. demographics, risk perception, and attribution of responsibilities) influence individuals’ pro-environmental behaviors. We also compare the effectiveness of BBN approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSIs). The results show that influencing factors for current adoption vary by different types of GSI. Risk perception of stormwater issues are associated with adoption of GSIs. Runoff issues are more likely to be considered as the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered as the residents’ own responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), BBN approach produces more accurate prediction compared to logistic regression.

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Park, Jinsun. « Bayesian decision theory and the justification of the admissibility requirement on degrees of belief / ». The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487590702989539.

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Yeung, Ping E. « Belief-revisions after earnings announcements : evidence from security analysts' forecast revisions / ». view abstract or download file of text, 2003. http://wwwlib.umi.com/cr/uoregon/fullcit?p3095287.

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Thesis (Ph. D.)--University of Oregon, 2003.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 77-82). Also available for download via the World Wide Web; free to University of Oregon users.

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Oteniya, Lloyd. « Bayesian belief networks for dementia diagnosis and other applications : a comparison of hand-crafting and construction using a novel data driven technique ». Thesis, University of Stirling, 2008. http://hdl.handle.net/1893/497.

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The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ''learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ''real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data.

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Punyamurthula, Sudhir. « BAYESIAN-INTEGRATED SYSTEM DYNAMICS MODELLING FOR PRODUCTION LINE RISK ASSESSMENT ». UKnowledge, 2018. https://uknowledge.uky.edu/me_etds/124.

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Companies, across the globe are concerned with risks that impair their ability to produce quality products at a low cost and deliver them to customers on time. Risk assessment, comprising of both external and internal elements, prepares companies to identify and manage the risks affecting them. Although both external/supply chain and internal/production line risk assessments are necessary, internal risk assessment is often ignored. Internal risk assessment helps companies recognize vulnerable sections of production operations and provide opportunities for risk mitigation. In this research, a novel production line risk assessment methodology is proposed. Traditional simulation techniques fail to capture the complex relationship amongst risk events and the dynamic interaction between risks affecting a production line. Bayesian- integrated System Dynamics modelling can help resolve this limitation. Bayesian Belief Networks (BBN) effectively capture risk relationships and their likelihoods. Integrating BBN with System Dynamics (SD) for modelling production lines help capture the impact of risk events on a production line as well as the dynamic interaction between those risks and production line variables. The proposed methodology is applied to an industrial case study for validation and to discern research and practical implications.

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Franco, Chiara. « Modelling the dynamics of CaCO3 budgets in changing environments using a Bayesian Belief Network approach ». Thesis, University of Essex, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654563.

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Multiple stressors on reefs are increasing the need for remedial actions to buffer anthropogenic pressure and reduce coral reef deterioration. In order to promote reef framework endurance, it is critical to identify and track down multiple stressor sources. To date, spatial and temporal variations of reef framework carbonate production and erosion have been estimated using carbonate budget assessments; however, these are limited in determining the extent to which various stressors are responsible for altering the budgetary state. This study has developed a Bayesian Network model (CARBNET) to identify and evaluate the extent to which anthropogenic and climatic disturbances affect coral reef budgetary state. The main adavantage of using this type of model for management purposes are related to its ability to adapt to changes and to quantify and incorporate uncertainty. In addition, it provides the opportunity to identify key gaps in the knowledge to inform future research priorities. Multi-scale scenario-based analyses, conducted for the Wakatobi (South-east Sulawesi, Indonesia) and Grenada (Caribbean) reefs, quantified the effects of multiple stressors on the reefal components, providing information on the actual state and possible future state of the framework. Reefs with high branching coral cover were likely to be found in a positive budgetary state, whilst low coral cover and reduced topographic complexity were associated with low carbonate production or negative budgetary state. In clear water settings, degraded reefs, characterised by high turbidity, sedimentation and nutrient concentrations, were likely to be found in a low carbonate production or erosional state. Conversely, high carbonate production was characteristic of reef environments with low turbidity, sedimentation and nutrient concentrations. At regional level, CARBNET predicted that reefs will accrete at a different pace; in Grenada reduced gross production and sustained erosion maintained the budget close to the equilibrium, whilst Wakatobi reefs were defined by positive budgetary states. At local level, reefs at shallow depths were likely to be associated with erosion or low positive net production in both regions, although in Indonesia high carbonate production offsets erosion at all sites. Anthropogenic and climatic disturbances acted synergistically in decreasing carbonate production, and degraded reefs with < 10% hard coral were predicted to be in an erosional state. This result suggests that degraded systems have lowered their tipping point to a net erosion shift. The benthic community was affected by sedimentation and elevated nutrients and changes in the key drivers of carbonate production resulted in reduced net carbonate production. External bioeroder densities were restrained by degradation of the nursery ground, whilst internal bioerosion increased in nutrient enriched waters. Overall, CARBNET is reliable in groundtruthing empirical data and is therefore a valuable addition to the reef management toolbox.

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Tang, Antony Shui Sum, et n/a. « A rationale-based model for architecture design reasoning ». Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070319.100952.

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Large systems often have a long life-span and their system and software architecture design comprise many intricately related elements. The verification and maintenance of these architecture designs require an understanding of how and why the system are constructed. Design rationale is the reasoning behind a design and it provides an explanation of the design. However, the reasoning is often undocumented or unstructured in practice. This causes difficulties in the understanding of the original design, and makes it hard to detect inconsistencies, omissions and conflicts without any explanations to the intricacies of the design. Research into design rationale in the past has focused on argumentation-based design deliberations. Argumentation-based design rationale models provide an explicit representation of design rationale. However, these methods are ineffective in communicating design reasoning in practice because they do not support tracing to design elements and requirements in an effective manner. In this thesis, we firstly report a survey of practising architects to understand their perception of the value of design rationale and how they use and document this knowledge. From the survey, we have discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated certain barriers to the use and documentation of design rationale. The results have indicated that there is no systematic approach to using and capturing design rationale in current architecture design practice. Using these findings, we address the issues of representing and applying architecture design rationale. We have constructed a rationale-based architecture model to represent design rationale, design objects and their relationships, which we call Architecture Rationale and Element Linkage (AREL). AREL captures both qualitative and quantitative rationale for architecture design. Quantitative rationale uses costs, benefits and risks to justify architecture decisions. Qualitative rationale documents the issues, arguments, alternatives and tradeoffs of a design decision. With the quantitative and qualitative rationale, the AREL model provides reasoning support to explain why architecture elements exist and what assumptions and constraints they depend on. Using a causal relationship in the AREL model, architecture decisions and architecture elements are linked together to explain the reasoning of the architecture design. Architecture Rationalisation Method (ARM) is a methodology that makes use of AREL to facilitate architecture design. ARM uses cost, benefit and risk as fundamental elements to rank and compare alternative solutions in the decision making process. Using the AREL model, we have proposed traceability and probabilistic techniques based on Bayesian Belief Networks (BBN) to support architecture understanding and maintenance. These techniques can help to carry out change impact analysis and rootcause analysis. The traceability techniques comprise of forward, backward and evolution tracings. Architects can trace the architecture design to discover the change impacts by analysing the qualitative reasons and the relationships in the architecture design. We have integrated BBN to AREL to provide an additional method where probability is used to evaluate and reason about the change impacts in the architecture design. This integration provides quantifiable support to AREL to perform predictive, diagnostic and combined reasoning. In order to align closely with industry practices, we have chosen to represent the rationale-based architecture model in UML. In a case study, the AREL model is applied retrospectively to a real-life bank payment systems to demonstrate its features and applications. Practising architects who are experts in the electronic payment system domain have been invited to evaluate the case study. They have found that AREL is useful in helping them understand the system architecture when they compared AREL with traditional design specifications. They have commented that AREL can be useful to support the verification and maintenance of the architecture because architects do not need to reconstruct or second-guess the design reasoning. We have implemented an AREL tool-set that is comprised of commercially available and custom-developed programs. It enables the capture of architecture design and its design rationale using a commercially available UML tool. It checks the well-formedness of an AREL model. It integrates a commercially available BBN tool to reason about the architecture design and to estimate its change impacts.

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Ordonez, Javier. « A methodology for project risk analysis using Bayesian belief networks within a Monte Carlo simulation environment ». College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6871.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Civil Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Spenkuch, Thomas. « A Bayesian belief network approach for modelling tactical decision-making in a multiple yacht race simulator ». Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/366587/.

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The importance of human factors has to be taken into account when determining a yacht’s performance over a course. The crew’s capabilities of technical skills, athletic performance, and his/her ability of making rational decisions under time pressure and in light of uncertainty of the future wind regime are important aspects that will determine the overall performance of a yacht-crew system. This thesis highlights the performance of such a yacht-crew system with a focus on the decision-making process of sailors. Aspects of human behaviour in sport and the decision-making process are explained considering the level of expertise and possible approaches of how to model them are shown. An artificial intelligence AI -system is developed that is capable of simulating the decision-making process of different sailing behaviours/styles as well as different expertise levels of sailors within a dynamically changing yacht racing environment. The constraints of the multiple fleet racing simulator Robo-Race (Scarponi 2008) were determined using a series of tests with real sailors identified three important constrains: (1) the predictable behaviour of the AI-yachts, (2) the predictable and unrealistic weather model and (3) the simple model describing the effects of yacht interaction. These restrictions and constraints that limited the real and AI-sailors natural sailing behaviour have been successfully removed in the updated version of Robo-Race. The new developed decision-making engine based on Decision Field Theory that uses Bayesian Belief Networks as the perceptual processor showed a clear superiority over the old rule-based decision-making engine. Extensive simulations demonstrate the feasibility of modelling various decision-making processes and therefore different behaviours and expertise levels of sailors. A good comparison was found with that obtained between the Robo-Race results and the Olympic fleet racing events.

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Dumas, Jeremiah Percy. « A spatial decision support system utilizing data from the Gap Analysis Program and a Bayesian Belief Network ». Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-07072005-104946.

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Anderson, Jerone S. « A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks ». Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1242921000.

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Shabarchin, Oleg. « Induced seismicity and corrosion vulnerability assessment of oil and gas pipelines using a Bayesian belief network model ». Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/57569.

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A substantial amount of oil and gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. Because of the adverse environmental impact and significant financial losses, the integrity of these pipelines is essential. British Columbia Oil and Gas Commission (BCOGC) has indicated metal loss due to corrosion as one of the primary causes of pipeline failures. Therefore, it is important to identify pipelines subjected to severe corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this thesis presents a Bayesian belief network (BBN)-based probabilistic corrosion hazard assessment approach for oil and gas pipelines. A cause-effect BBN model has been developed by considering various types of information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network in order to estimate corrosion defects and the associated probability of failure. Besides corrosion hazard, BCOGC has reported multiple cases of anthropogenic seismicity, which also may compromise the pipeline integrity. To this end, this thesis explores the potential impact of induced seismicity on the oil and gas pipeline infrastructure. Spatial clustering analysis is used for earthquakes, previously registered in the region, to delineate areas, which are particularly prone to the induced seismicity. The state of the art ground motion prediction equation for induced seismicity is applied in a Monte Carlo simulation to obtain a stochastic field of the seismic intensity. Based on the established seismic fragility formulations for pipelines and mechanical characteristics as well as corrosion conditions, spatial and probabilistic distributions of the repair rate and probability of failure have been obtained and visualized with the aid of the Geographic Information System. The proposed model can help to identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision making process. To demonstrate the application of the proposed approach, two case studies for the Northeastern British Columbia oil and gas pipeline infrastructure are presented.
Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate

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Morgenroth, Josephine S. « Elastic stress modelling and prediction of ground class using a Bayesian Belief Network at the Kemano tunnels ». Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58971.

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The Kemano hydroelectric facility was constructed in the 1950s to supply power to the aluminum smelter in Kitimat, on the west coast of British Columbia. The Kemano project includes a 16 km long water conveyance tunnel that set world record advance rates in the 1950s, and 8 km of a partially completed tunnel. A risk management strategy was developed in the late 1980s in case of collapse of the first water conveyance tunnel, and by 1990 the excavation of a second tunnel parallel to the first had begun. Work halted in 1991 due to environmental litigation and change in political climate. In 2011 the owner of the Kitimat smelter and the Kemano hydroelectric facility announced plans to continue work on the tunnel that was left unfinished. This thesis is a collaboration with Hatch Ltd., a consultant to the owner, to determine the ground conditions and support requirements that should be anticipated in completing the backup tunnel. Three dimensional finite element elastic stress modelling was completed in order to determine the in-situ stress conditions as well as the boundary stresses around the tunnel. The modelling results were used to estimate where stress-induced problem areas should be expected, for example at chainages 10+700 to 12+700 in the backup tunnel. The results of the stress modelling were incorporated into a Bayesian Belief Network that was developed for the Kemano tunnels. It was built using widely accepted empirical relationships in rock mechanics, expert judgement and conditional relationships between inputs. This network predicts the ground class at a user-defined chainage, based on a database that was developed from project literature. The user is also able to input new data as it becomes available, for example during the tunnel advance. The predictions from the network align with what can be seen in the excavated portion of the backup tunnel, for example accurately predicting the need for steel sets at chainage 8+510. The predicted ground class was plotted as a function of chainage, and may be used as a comparison to the support requirements that have been determined thus far.
Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate

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Selvatici, Antonio Henrique Pinto. « Construção de mapas de objetos para navegação de robôs ». Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-01072009-153749/.

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Como a complexidade das tarefas realizadas por robôs móveis vêm aumentando a cada dia, a percepção do robô deve ser capaz de capturar informações mais ricas do ambiente, que permitam a tomada de decisões complexas. Entre os possíveis tipos de informação que podem ser obtidos do ambiente, as informações geométricas e semânticas têm papéis importantes na maioria das tarefas designadas a robôs. Enquanto as informações geométricas revelam como os objetos e obstáculos estão distribuídos no espaço, as informações semânticas capturam a presença de estruturas complexas e eventos em andamento no ambiente, e os condensam em descrições abstratas. Esta tese propõe uma nova técnica probabilística para construir uma representação do ambiente baseada em objetos a partir de imagens capturadas por um robô navegando com uma câmera de vídeo solidária a ele. Esta representação, que fornece descrições geométricas e semânticas de objetos, é chamada O-Map, e é a primeira do gênero no contexto de navegação de robôs. A técnica de mapeamento proposta é também nova, e resolve concomitantemente os problemas de localização, mapeamento e classificação de objetos, que surgem quando da construção de O-Maps usando imagens processadas por detectores imperfeitos de objetos e sem um sensor de localização global. Por este motivo, a técnica proposta é chamada O-SLAM, e é o primeiro algoritmo que soluciona simultaneamente os problemas de localização e mapeamento usando somente odometria e o resultado de algoritmos de reconhecimento de objetos. Os resultados obtidos através da aplicação de O-SLAM em imagens processadas por uma técnica simples de detecção de objetos mostra que o algoritmo proposto é capaz de construir mapas que descrevem consistentemente os objetos do ambiente, dado que o sistema de visão computacional seja capaz de detectá-los regularmente. Em particular, O-SLAM é eficaz em fechar voltas grandes na trajetória do robô, e obtém sucesso mesmo se o sistema de detecção de objetos posuir falhas, relatando falsos positivos e errando a classe do objeto algumas vezes, consertando estes erros. Dessa forma, O-SLAM é um passo em direção à solução integrada do problema de localização, mapeamento e reconhecimento de objetos, a qual deve prescindir de um sistema pronto de reconhecimento de objetos e gerar O-Maps somente pela fusão de informações geométricas e visuais obtidas pelo robô.
As tasks performed by mobile robots are increasing in complexity, robot perception must be able to capture richer information from the environment in order to allow complex decision making. Among the possible types of information that can be retrieved from the environment, geometric and semantic information play important roles in most of the tasks assigned to robots. While geometric information reveals how objects and obstacles are distributed in space, semantic information captures the presence of complex structures and ongoing events from the environment and summarize them in abstract descriptions. This thesis proposes a new probabilistic technique to build an object-based representation of the robot surrounding environment using images captured by an attached video camera. This representation, which provides geometric and semantic descriptions of the objects, is called O-Map, and is the first of its kind in the robot navigation context. The proposed mapping technique is also new, and concurrently solves the localization, mapping and object classification problems arisen from building O-Maps using images processed by imperfect object detectors and no global localization sensor. Thus, the proposed technique is called O-SLAM, and is the first algorithm to solve the simultaneous localization and mapping problem using solely odometers and the output from object recognition algorithms. The results obtained by applying O-SLAM to images processed by simple a object detection technique show that the proposed algorithm is able to build consistent maps describing the objects in the environment, provided that the computer vision system is able to detect them on a regular basis. In particular, O-SLAM is effective in closing large loops in the trajectory, and is able to perform well even if the object detection system makes spurious detections and reports wrong object classes, fixing these errors. Thus, O-SLAM is a step towards the solution of the simultaneous localization, mapping and object recognition problem, which must drop the need for an off-the-shelf object recognition system and generate O-Maps only by fusing geometric and appearance information gathered by the robot.

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Li, Mingmei. « Application of the Bayesian belief network model to evaluate variances in a clinical caremap : Radical prostatectomy case study ». Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26693.

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A clinical caremap is a cost-effective tool for clinical process improvement that has been accepted in hospitals and various healthcare organizations in many countries. However, compared to the literature describing the initial development of the clinical caremaps, the evaluation of the impact of the variances in the caremap pathway on the patient's expected outcomes and the patient's length of stay (LOS) remains relatively less analyzed. In this research, we deal with the issue of variances in the clinical caremap by building a Bayesian belief network named BBN_RPC to model the radical prostatectomy caremap. The BBN_RPC model provides insight into probabilistic dependencies that exist among the activities (variables) in the caremap. We then use the BBN_RPC model to analyze possible variances and to make inferences. The results show that most of the activities in the caremap are related with each other and to some extent linked with the patient's length of stay (LOS), whereas different activities have different weights on the LOS. Using radical prostatectomy patients' data from a retrospective chart study conducted at the Ottawa Civic Hospital, we have applied the BBN_RPC model to predict a patient's future conditions and the LOS, based on the current observations. Predictive accuracy of the BBN_RPC model was evaluated by cross validation tests, which showed the accuracy for predicting the patient's LOS, given the patient's observations during the first two post-op days, is at approximately 94% level.

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