Relevant bibliographies by topics / Probabilistic Bayesian Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probabilistic Bayesian Network'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Probabilistic Bayesian Network"

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Motomura, Yoichi. "Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 93–99. http://dx.doi.org/10.20965/jaciii.2004.p0093.

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Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.
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TERZIYAN, VAGAN. "A BAYESIAN METANETWORK." International Journal on Artificial Intelligence Tools 14, no. 03 (2005): 371–84. http://dx.doi.org/10.1142/s0218213005002156.

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Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of contextual features' values. The main challenge of this paper is the multilevel probabilistic meta-model (Bayesian Metanetwork), which is an extension of traditional BN and modification of recursive multinets. It assumes that interoperability between comp
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LIU, WEI-YI, and KUN YUE. "BAYESIAN NETWORK WITH INTERVAL PROBABILITY PARAMETERS." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 911–39. http://dx.doi.org/10.1142/s0218213011000449.

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Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditi
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Herskovits, E. H., and G. F. Cooper. "Algorithms for Bayesian Belief-Network Precomputation." Methods of Information in Medicine 30, no. 02 (1991): 81–89. http://dx.doi.org/10.1055/s-0038-1634820.

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AbstractBayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.We first present the necessary background, and then present algorithms for producin
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VÉRONIQUE, DELCROIX, MAALEJ MOHAMED-AMINE, and PIECHOWIAK SYLVAIN. "BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES." International Journal on Artificial Intelligence Tools 16, no. 03 (2007): 417–33. http://dx.doi.org/10.1142/s0218213007003345.

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Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.
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PENG, YUN, ZHONGLI DING, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REVISION WITH PROBABILISTIC CONSTRAINTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 03 (2012): 317–37. http://dx.doi.org/10.1142/s021848851250016x.

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This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability distribution of the revised BN is as close as possible to that of the original BN. Two variations of E-IPF
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Riali, Ishak, Messaouda Fareh, and Hafida Bouarfa. "Fuzzy Probabilistic Ontology Approach." International Journal on Semantic Web and Information Systems 15, no. 4 (2019): 1–20. http://dx.doi.org/10.4018/ijswis.2019100101.

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In spite of the undeniable success of the ontologies, where they have been widely applied successfully to represent the knowledge in lots of real-world problems, they cannot represent and reason with uncertain knowledge which inherently appears in most domains. To cope with this issue, this article presents a new approach for dealing with rich-uncertainty domains. In fact, it is mainly based on integrating hybrid models which combine both fuzzy logic and Bayesian networks. On the other hand, the Fuzzy multi-entity Bayesian network (FzMEBN) proposed as a hybrid model which enhances the classica
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Singh, Vikash, Matthew Khanzadeh, Vincent Davis, et al. "Bayesian Binary Search." Algorithms 18, no. 8 (2025): 452. https://doi.org/10.3390/a18080452.

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We present Bayesian Binary Search (BBS), a novel framework that bridges statistical learning theory/probabilistic machine learning and binary search. BBS utilizes probabilistic methods to learn the underlying probability density of the search space. This learned distribution then informs a modified bisection strategy, where the split point is determined by probability density rather than the conventional midpoint. This learning process for search space density estimation can be achieved through various supervised probabilistic machine learning techniques (e.g., Gaussian Process Regression, Bay
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Zhu, Xianyou, and Songlin Tang. "Design of an Artificial Intelligence Algorithm Teaching System for Universities Based on Probabilistic Neuronal Network Model." Scientific Programming 2022 (April 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/4131058.

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Intelligence is gradually becoming an important tool for solving difficult problems with the development of computers. This article takes the design of university teaching systems as the research context to establish an artificial intelligence network research and learning platform. A probabilistic process neuron network model is proposed, which combines the Bayesian probabilistic classification mechanism with the dynamic signal processing method of process neuron networks, and achieves dynamic classification based on Bayesian rules by adding a pattern unit layer to the feed-forward process ne
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Su, Jie, Jun Li, and Jifeng Chen. "Probabilistic Graph Model Mining User Affinity in Social Networks." International Journal of Web Services Research 18, no. 3 (2021): 22–41. http://dx.doi.org/10.4018/ijwsr.2021070102.

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In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity i
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Dissertations / Theses on the topic "Probabilistic Bayesian Network"

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Sahin, Elvan. "Discrete-Time Bayesian Networks Applied to Reliability of Flexible Coping Strategies of Nuclear Power Plants." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103817.

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The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risky situations in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by Nuclear Energy Institute (NEI) to manage the challenge of BDBEEs and to enhance reactor safety against extended station blackout (SBO). To assess the effectiveness of FLEX strategies, probabilistic risk assessment (PRA) methods can be used to calculate the reliability of such systems. Due to the uniquen
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Yoo, Keunyoung. "Probabilistic SEM : an augmentation to classical Structural equation modelling." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66521.

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Structural equation modelling (SEM) is carried out with the aim of testing hypotheses on the model of the researcher in a quantitative way, using the sampled data. Although SEM has developed in many aspects over the past few decades, there are still numerous advances which can make SEM an even more powerful technique. We propose representing the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things a step further and conduct inference by explicitly entering evidence into the network
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Zhao, Wenyu. "A Probabilistic Approach for Prognostics of Complex Rotary Machinery Systems." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1423581651.

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Björkman, Peter. "Probabilistic Safety Assessment using Quantitative Analysis Techniques : Application in the Heavy Automotive Industry." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-163262.

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Safety is considered as one of the most important areas in future research and development within the automotive industry. New functionality, such as driver support and active/passive safety systems are examples where development mainly focuses on safety. At the same time, the trend is towards more complex systems, increased software dependence and an increasing amount of sensors and actuators, resulting in a higher risk associated with software and hardware failures. In the area of functional safety, standards such as ISO 26262 assess safety mainly focusing on qualitative assessment technique
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Quer, Giorgio. "Optimization of Cognitive Wireless Networks using Compressive Sensing and Probabilistic Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421992.

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In-network data aggregation to increase the efficiency of data gathering solutions for Wireless Sensor Networks (WSNs) is a challenging task. In the first part of this thesis, we address the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a Data Collection Point (DCP). We exploit Principal Component Analysis (PCA) to learn the relevant statistical characteristics of the signals of interest at the DCP. Then, at the DCP we use this knowledge to design a matrix required by the recovery techniques, that exploit convex optimization (Co
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Ramani, Shiva Shankar. "Graphical Probabilistic Switching Model: Inference and Characterization for Power Dissipation in VLSI Circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000497.

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Bortolini, Rafaela. "Enhancing building performance : a Bayesian network model to support facility management." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666187.

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The performance of existing buildings is receiving increased concern due to the need to renovate the aging building stock and provide better quality of life for end users. The conservation state of buildings and the indoor environment conditions have been related to occupants’ well-being, health, and productivity. At the same time, there is a need for more sustainable buildings with reduced energy consumption. Most challenges encountered during the analysis of the performance of existing buildings are associated with the complex relationships among the causal factors involved. The performance
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Klukowski, Piotr. "Nuclear magnetic resonance spectroscopy interpretation for protein modeling using computer vision and probabilistic graphical models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4720.

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Dynamic development of nuclear magnetic resonance spectroscopy (NMR) allowed fast acquisition of experimental data which determine structure and dynamics of macromolecules. Nevertheless, due to lack of appropriate computational methods, NMR spectra are still analyzed manually by researchers what takes weeks or years depending on protein complexity. Therefore automation of this process is extremely desired and can significantly reduce time of protein structure solving. In presented work, a new approach to automated three-dimensional protein NMR spectra analysis is presented. It is based on Hist
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Ramalingam, Nirmal Munuswamy. "A complete probabilistic framework for learning input models for power and crosstalk estimation in VLSI circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000505.

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Tran, Thanh Binh. "A Bayesian Network framework for probabilistic identification of model parameters from normal and accelerated tests : application to chloride ingress into conrete." Nantes, 2015. https://archive.bu.univ-nantes.fr/pollux/show/show?id=1bd3c7d5-c357-43f1-b430-bb5e97e9ef3c.

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La pénétration des chlorures dans le béton est l'une des causes principales de dégradation des ouvrages en béton armé. Sous l’attaque des chlorures des dégradations importantes auront lieu après 10 à 20 ans. Par conséquent, ces ouvrages devraient être périodiquement inspectés et réparés afin d’assurer des niveaux optimaux de capacité de service et de sécurité pendant leur durée de vie. Des paramètres matériels et environnementaux pertinents peuvent être déterminés à partir des données d’inspection. En raison de la cinétique longue des mécanismes de pénétration de chlorures et des difficultés p
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Books on the topic "Probabilistic Bayesian Network"

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Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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A, Gammerman, and UNICOM Seminars, eds. Probabilistic reasoning and Bayesian belief networks. Alfred Waller in association with UNICOM, 1995.

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Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470091754.

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Taroni, Franco, Alex Biedermann, Silvia Bozza, Paolo Garbolino, and Colin Aitken. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118914762.

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1955-, Lucas Peter, Gámez José A, and Salmerón Antonio, eds. Advances in probabilistic graphical models. Springer, 2007.

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Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. Wiley & Sons, Limited, John, 2006.

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Cowell, Robert G., David J. Spiegelhalter, Steffen L. Lauritzen, and Philip Dawid. Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks. Springer London, Limited, 2006.

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Probabilistic methods for bionformatics: With an introduction to Bayesian networks. Morgan Kaufmann Publishers, 2009.

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Probabilistic Reasoning and Bayesian Belief Networks (UNICOM - Information & Communications Technology). Nelson Thornes Ltd, 1998.

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Neapolitan, Richard E. Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks. Elsevier Science & Technology Books, 2009.

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Book chapters on the topic "Probabilistic Bayesian Network"

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Butz, Cory J., Jhonatan de S. Oliveira, and Anders L. Madsen. "Bayesian Network Inference Using Marginal Trees." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_6.

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. "Learning Bayesian Networks." In Expert Systems and Probabilistic Network Models. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5_11.

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Land, Walker H., and J. David Schaffer. "Bayesian Probabilistic Neural Network (BPNN)." In The Art and Science of Machine Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18496-4_7.

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Muller, Alexandre, Marie-Christine Suhner, and Benoît Iung. "Bayesian Network-based Proactive Maintenance." In Probabilistic Safety Assessment and Management. Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_332.

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Ben Mrad, Ali, Véronique Delcroix, Sylvain Piechowiak, and Philip Leicester. "From Information to Evidence in a Bayesian Network." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_3.

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Kraisangka, Jidapa, and Marek J. Druzdzel. "Discrete Bayesian Network Interpretation of the Cox’s Proportional Hazards Model." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_16.

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Suzuki, Joe. "Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_31.

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Zhang, Chenjing, Kun Yue, Jinghua Zhu, Xiaoling Wang, and Aoying Zhou. "Bayesian Network-Based Probabilistic XML Keywords Filtering." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29023-7_28.

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Zhou, Yun, Norman Fenton, and Martin Neil. "An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints." In Probabilistic Graphical Models. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_38.

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Wicker, Matthew, Andrea Patane, Luca Laurenti, and Marta Kwiatkowska. "Adversarial Robustness Certification for Bayesian Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71162-6_1.

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AbstractWe study the problem of certifying the robustness of Bayesian neural networks (BNNs) to adversarial input perturbations. Specifically, we define two notions of robustness for BNNs in an adversarial setting: probabilistic robustness and decision robustness. The former deals with the probabilistic behaviour of the network, that is, it ensures robustness across different stochastic realisations of the network, while the latter provides guarantees for the overall (output) decision of the BNN. Although these robustness properties cannot be computed analytically, we present a unified computa
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Conference papers on the topic "Probabilistic Bayesian Network"

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Jain, Swati, Francois Ayello, John A. Beavers, and Narasi Sridhar. "Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks." In CORROSION 2013. NACE International, 2013. https://doi.org/10.5006/c2013-02616.

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Abstract Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, the uncertainty in data makes the prediction of SCC challenging. The complex interactions that affect SCC failure probability can be modeled using Bayesian network models. The B
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Prashal, Garima, Parasuraman Sumathi, and Narayana Prasad Padhy. "Interpretable Deep Bayesian Neural Network for Probabilistic Power Flow Analysis." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689085.

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Sridhar, Narasi, Francois Ayello, Theresa Stewart, and Ali Mosleh. "Probabilistic Assessment of Hydrogen Transportation System Components." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00121.

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Abstract Many countries are planning to transport hydrogen through existing natural gas pipeline infrastructure. The integrity of pipelines for hydrogen service has been examined extensively, but other components in the system, such as valves, compressors, pressure regulators, and many other discrete devices, have not been evaluated to any great extent. This paper presents a uniform approach to modeling the performance of these components under hydrogen blend service using a Bayesian network method. The baseline failure rate of system components under natural gas transport can be derived from
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Ayello, Francois, Tony Alfano, Davion Hill, and Narasi Sridhar. "A Bayesian Network Based Pipeline Risk Management." In CORROSION 2012. NACE International, 2012. https://doi.org/10.5006/c2012-01123.

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Abstract Risk is defined as a product of the probability of a hazard causing an adverse event combined with the severity (or consequences) of that adverse event. Accordingly, pipeline risk management should integrate both the concepts of failure frequency and the potential consequences for each hazard scenario. Pipelines’ risk assessment is particularly challenging because pipelines cover extended geographic regions and there are numerous threats to pipeline integrity. Consequences are not always easy to evaluate depending on many parameters such as type of product being transported and terrai
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Guan, Shan, Francois Ayello, Narasi Sridhar, Jianping Liu, and Qingshan Feng. "Development of a Probabilistic Model for Assessing Pipeline Third Party Damage Threats." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-12719.

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Abstract Third Party Damage (TPD) represents the largest threat to the integrity of onshore oil and gas pipelines. There exist needs for developing reliable models that can quantify the probability of pipeline exposure to TPD threats. Bayesian networks (BN) modeling possesses the advantage to quantify the uncertainties and identify where the reduction of these uncertainties has the greatest benefit in terms of the overall failure. This paper reports a modeling approach using Bayesian Network to quantify the Mechanical damage (a major form of TPD) threats to pipeline. Case studies to exam excav
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Jain, Swati, Andrea N. Sánchez, Shan Guan, et al. "Probabilistic Assessment of External Corrosion Rates in Buried Oil and Gas Pipelines." In CORROSION 2015. NACE International, 2015. https://doi.org/10.5006/c2015-05529.

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Abstract Quantitative risk assessment due to external corrosion requires an estimation of corrosion rates which is a challenging task for pipeline engineers because of the uncertainty in data related to environmental and physical variables such as soil type, drainage, soil chemistry, CP effectiveness, coating type and coating properties. Unfortunately, the research into quantitative assessment of external corrosion rates and the probability of failure of a buried pipeline is limited and has not progressed significantly. The reason is the complex mechanism of external corrosion, numerous factor
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Sridhar, Narasi, and Mariano A. Kappes. "Probabilistic Assessment of Hydrogen Stress Cracking of Steels and CRA in Sour Environments." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20409.

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Abstract Hydrogen Stress Cracking (HSC) of steels and CRA has been studied for a long time and it is recognized that the results have significant variability depending on many factors, including test procedures, material microstructures, and environmental variables. Although the standards used for acceptance of a material for sour service are deterministic, the variabilities in the test data as well as field conditions merit probabilistic treatment. This paper provides a Bayesian network model of HSC that connects the variabilities in material, environment, and loading conditions through mecha
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Guan, Shan, Francois Ayello, Narasi Sridhar, Xiaoming Han, Qingshan Feng, and Yonghe Yang. "Application of Probabilistic Model in Pipeline Direct Assessment." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-12718.

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Abstract Direct assessment (DA) is a valuable tool for risk management used across the pipeline industry. To carry out DA, pipeline operators usually follow NACE(1) standards such as NACE SP0502, SP0206 or SP 0208. Each of these standards requires utilizing an appropriate model to assess and predict pipeline corrosion rate. One type of model that attracts attention in recent years is probabilistic models especially those created based on Bayesian Networks. Bayesian Networks is particularly well suited to help the DA process because the methodology allows combining mechanistic models with exper
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Moura, Gabriel, and Mauro Roisenberg. "Probabilistic Fuzzy Bayesian Network." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7381989.

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Goštautaitė, Daiva. "DYNAMIC LEARNING STYLE MODELLING USING PROBABILISTIC BAYESIAN NETWORK." In 11th International Conference on Education and New Learning Technologies. IATED, 2019. http://dx.doi.org/10.21125/edulearn.2019.0781.

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Reports on the topic "Probabilistic Bayesian Network"

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Moler, Edward J., and I. S. Mian. Analysis of molecular expression patterns and integration with other knowledge bases using probabilistic Bayesian network models. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/753888.

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Groth, Katrina, and Laura Swiler. Use of limited data to construct Bayesian networks for probabilistic risk assessment. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095131.

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Utsugi, Akio, and Motoyuki Akamatsu. Analysis of Car-Following Behavior Using Dynamic Probabilistic Models~Identification of Driving Mode Transition Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0241.

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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement typ
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