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Relevant bibliographies by topics / Bayesian belief

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Academic literature on the topic 'Bayesian belief'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Bayesian belief"

1

Marrero, Osvaldo. "What is Bayesian statistics?" Mathematical Gazette 100, no. 548 (June 14, 2016): 247–56. http://dx.doi.org/10.1017/mag.2016.61.

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Bayesian statistics is included in few elementary statistics courses, and many mathematicians have heard of it, perhaps through collateral readings from popular literature or [1], selected as an Editor's Choice in the New York Times Book Review. ‘Bayesian statistics’ provides for a way to incorporate prior beliefs, experience, or information into the analysis of data. Bayesian thinking is natural, and that is an advantage. For example, on a summer morning, if we see dark rain clouds up in the sky, we leave home for work with an umbrella because prior experience tells us that doing so is beneficial. In general, the idea is simple; schematically, it looks like this:(prior belief) + (data: new information) ⇒ (posterior belief).Thus, we begin with a prior belief that we allow to be modified or informed by new data to produce a posterior belief, which then becomes our new prior, and this process is never-ending. We are always willing to update our beliefs according to new information.

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Dietrich, Franz. "Bayesian group belief." Social Choice and Welfare 35, no. 4 (April 29, 2010): 595–626. http://dx.doi.org/10.1007/s00355-010-0453-x.

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LIN, YAN, and MAREK J. DRUZDZEL. "RELEVANCE-BASED INCREMENTAL BELIEF UPDATING IN BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (March 1999): 285–95. http://dx.doi.org/10.1142/s0218001499000161.

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Relevance reasoning in Bayesian networks can be used to improve efficiency of belief updating algorithms by identifying and pruning those parts of a network that are irrelevant for computation. Relevance reasoning is based on the graphical property of d-separation and other simple and efficient techniques, the computational complexity of which is usually negligible when compared to the complexity of belief updating in general. This paper describes a belief updating technique based on relevance reasoning that is applicable in practical systems in which observations and model revisions are interleaved with belief updating. Our technique invalidates the posterior beliefs of those nodes that depend probabilistically on the new evidence or the revised part of the model and focuses the subsequent belief updating on the invalidated beliefs rather than on all beliefs. Very often observations and model updating invalidate only a small fraction of the beliefs and our scheme can then lead to sub stantial savings in computation. We report results of empirical tests for incremental belief updating when the evidence gathering is interleaved with reasoning. These tests demonstrate the practical significance of our approach.

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Augenblick, Ned, and Matthew Rabin. "Belief Movement, Uncertainty Reduction, and Rational Updating*." Quarterly Journal of Economics 136, no. 2 (February 3, 2021): 933–85. http://dx.doi.org/10.1093/qje/qjaa043.

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Abstract When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

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Chambers, Christopher P., and Takashi Hayashi. "Bayesian consistent belief selection." Journal of Economic Theory 145, no. 1 (January 2010): 432–39. http://dx.doi.org/10.1016/j.jet.2009.07.001.

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Tang, Qianfeng. "Hierarchies of beliefs and the belief-invariant Bayesian solution." Journal of Mathematical Economics 59 (August 2015): 111–16. http://dx.doi.org/10.1016/j.jmateco.2015.06.006.

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Liu, Qingmin. "Stability and Bayesian Consistency in Two-Sided Markets." American Economic Review 110, no. 8 (August 1, 2020): 2625–66. http://dx.doi.org/10.1257/aer.20181186.

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We propose a criterion of stability for two-sided markets with asymmetric information. A central idea is to formulate off-path beliefs conditional on counterfactual pairwise deviations and on-path beliefs in the absence of such deviations. A matching-belief configuration is stable if the matching is individually rational with respect to the system of on-path beliefs and is not blocked with respect to the system of off-path beliefs. The formulation provides a language for assessing matching outcomes with respect to their supporting beliefs and opens the door to further belief-based refinements. The main refinement analyzed in the paper requires the Bayesian consistency of on-path and off-path beliefs with prior beliefs. We define concepts of Bayesian efficiency, the rational expectations competitive equilibrium, and the core. Their contrast with pairwise stability manifests the role of information asymmetry in matching formation. (JEL C78, D40, D82, D83)

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Shek, T. W. "Bayesian Belief Network in histopathology." Journal of Clinical Pathology 49, no. 10 (October 1, 1996): 864. http://dx.doi.org/10.1136/jcp.49.10.864-b.

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Jaffray, J. Y. "Bayesian updating and belief functions." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 5 (1992): 1144–52. http://dx.doi.org/10.1109/21.179852.

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Pinheiro de Cristo, Marco Antônio, Pável Pereira Calado, Maria de Lourdes da Silveira, Ilmério Silva, Richard Muntz, and Berthier Ribeiro-Neto. "Bayesian belief networks for IR." International Journal of Approximate Reasoning 34, no. 2-3 (November 2003): 163–79. http://dx.doi.org/10.1016/j.ijar.2003.07.006.

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Dissertations / Theses on the topic "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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Books on the topic "Bayesian belief"

1

Marshall, Adele Heather. Bayesian belief networks using conditional phase-type distibutions. [s.l: The Author], 2001.

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Pershad, Rinku. A Bayesian belief network for corporate credit risk assessment. Ottawa: National Library of Canada, 2000.

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3

Brian S. G. E. Sahely. Development of a bayesian belief network for anaerobic wastewater treatment. Ottawa: National Library of Canada, 2000.

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4

Social capital modeling in virtual communities: Bayesian belief network approaches. Hershey, PA: Information Science Reference, 2009.

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5

Epstein, Larry G. Dynamically consistent beliefs must be Bayesian. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1992.

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6

Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. Urbana, Ill: College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois at Urbana-Champaign, 1990.

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Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. Urbana, Ill: College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois at Urbana-Champaign, 1990.

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Feldman, Mark. On the generic nonconvergence of Bayesian actions and beliefs. [Urbana, Ill]: College of Commerce and Business Administration, University of Illinois Urbana-Champaign, 1989.

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9

A, Gammerman, and UNICOM Seminars, eds. Probabilistic reasoning and Bayesian belief networks. Henley-on-Thames: Alfred Waller in association with UNICOM, 1995.

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Ramoni, Marco, and Paolo Sebastiani. Theory and Practice of Bayesian Belief Networks. A Hodder Arnold Publication, 2001.

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Book chapters on the topic "Bayesian belief"

1

Trendowicz, Adam, and Ross Jeffery. "Bayesian Belief Networks (BBN)." In Software Project Effort Estimation, 339–48. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_14.

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Garrett, Anthony J. M. "Belief and Desire." In Maximum Entropy and Bayesian Methods, 175–86. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8729-7_13.

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Jensen, Finn V. "Belief Updating in Bayesian Networks." In Bayesian Networks and Decision Graphs, 159–200. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3502-4_5.

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Chung, Ji Ryang, and Gangman Yi. "Belief Propagation in Bayesian Network." In Lecture Notes in Electrical Engineering, 353–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41674-3_51.

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Vreeswijk, Gerard A. W. "Argumentation in Bayesian Belief Networks." In Lecture Notes in Computer Science, 111–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32261-0_8.

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Grover, Jeff. "Bayesian Belief Networks Experimental protocol." In The Manual of Strategic Economic Decision Making, 59–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48414-3_4.

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Joshi, A. V., S. C. Sahasrabudhe, and K. Shankar. "Bayesian approximation and invariance of Bayesian belief functions." In Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 251–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60112-0_29.

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Grover, Jeff. "Bayesian Belief Networks (BBN) Experimental Protocol." In Strategic Economic Decision-Making, 43–48. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6040-4_4.

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Ni, Zhifang, Lawrence D. Phillips, and George B. Hanna. "Evidence Synthesis Using Bayesian Belief Networks." In Evidence Synthesis in Healthcare, 155–68. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-206-3_7.

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Corney, David. "Designing Food with Bayesian Belief Networks." In Evolutionary Design and Manufacture, 83–94. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0519-0_7.

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Conference papers on the topic "Bayesian belief"

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Caticha, Ariel, Paul M. Goggans, and Chun-Yong Chan. "Quantifying Rational Belief." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275647.

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Ogutcu, Gokcen. "Pipeline Risk Assessment by Bayesian Belief Network." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10088.

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This study focuses on identification of risk factors in pipeline system and also, concentrates on identification of relationship between parameters. In order to achieve this purpose, Bayesian Belief Network with historical data was used to provide a framework for assessing risk relative to the company’s petroleum pipeline system. Each of the variables in the Bayesian Belief Network is described by nodes and each node has a state. Relationships between parameters are presented by arrows. Probability of any node being in state was shown in conditional probability tables. Historical data were helpful to build conditional probability tables. Variables were defined as corrosion, third party damage, mechanical and operational failure.

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Kestner, Brian, Christopher Perullo, Jeff Schutte, and Dimitri Mavris. "Integrated System Design Using Bayesian Belief Networks." In 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-617.

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Zhang, Jinqing, Haosong Yue, Xingming Wu, and Weihai Chen. "A brief review of Bayesian belief network." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8832649.

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Jamali, Mohsin M., and Golrokh Mirzaei. "Bayesian Belief Network Based Occupancy Assessment Framework." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645161.

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Savickas, Titas, and Olegas Vasilecas. "Bayesian belief network application in process mining." In the 15th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2659532.2659607.

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Chawlom, Tharaporn, and Sartra Wongthanavasu. "SET Index Forecast Using Bayesian Belief Networks." In 2020 12th International Conference on Knowledge and Smart Technology (KST). IEEE, 2020. http://dx.doi.org/10.1109/kst48564.2020.9059325.

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Hwang, Chien-Shung, Wei-Chung Lin, Chin-Tu Chen, and Shiuh-Yung J. Chen. "Bayesian belief networks for medical image recognition." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Raj S. Acharya and Dmitry B. Goldgof. SPIE, 1993. http://dx.doi.org/10.1117/12.148674.

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Goubanova, Olga, and Simon King. "Predicting consonant duration with Bayesian belief networks." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-607.

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Kharya, Shweta, Sunita Soni, and Tripti Swarnkar. "Weighted Bayesian Association Rule Mining Algorithm to Construct Bayesian Belief Network." In 2019 International Conference on Applied Machine Learning (ICAML). IEEE, 2019. http://dx.doi.org/10.1109/icaml48257.2019.00013.

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Reports on the topic "Bayesian belief"

1

Mislevy, Robert J. Virtual Representation of IID Observations in Bayesian Belief Networks. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada280552.

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McFarland, John, and Laura Painton Swiler. Validation of the thermal challenge problem using Bayesian Belief Networks. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/875636.

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Reed, Aaaron T. Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada378859.

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Hossain, Niamat Ullah Ibne, Farjana Nur, Raed Jaradat, Seyedmohsen Hosseini, Mohammad Marufuzzaman, Stephen Puryear, and Randy Buchanan. Metrics for assessing overall performance of inland waterway ports : a Bayesian Network based approach. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40545.

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Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports.

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