Relevant bibliographies by topics / Bayesian belief network

Academic literature on the topic 'Bayesian belief network'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Bayesian belief network"

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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 producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.
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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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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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Yershov, S. V., and F. V. Kostukevich. "Modeling technology based on fuzzy object-oriented Bayesian belief networks." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 179–87. http://dx.doi.org/10.15407/pp2016.02-03.179.

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The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks.
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Xu, Jian-min, Shu-fang Wu, and Yu Hong. "Topic tracking with Bayesian belief network." Optik 125, no. 9 (May 2014): 2164–69. http://dx.doi.org/10.1016/j.ijleo.2013.10.044.

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Grimm, Veronika, and Friederike Mengel. "Experiments on Belief Formation in Networks." Journal of the European Economic Association 18, no. 1 (October 9, 2018): 49–82. http://dx.doi.org/10.1093/jeea/jvy038.

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Abstract We study belief formation in social networks using a laboratory experiment. Participants in our experiment observe an imperfect private signal on the state of the world and then simultaneously and repeatedly guess the state, observing the guesses of their network neighbors in each period. Across treatments we vary the network structure and the amount of information participants have about the network. Our first result shows that information about the network structure matters and in particular affects the share of correct guesses in the network. This is inconsistent with the widely used naive (deGroot) model. The naive model is, however, consistent with a larger share of individual decisions than the competing Bayesian model, whereas both models correctly predict only about 25%–30% of consensus beliefs. We then estimate a larger class of models and find that participants do indeed take network structure into account when updating beliefs. In particular they discount information from neighbors if it is correlated, but in a more rudimentary way than a Bayesian learner would.
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PENG, YUN, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 05 (October 2010): 539–64. http://dx.doi.org/10.1142/s0218488510006696.

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This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes). Each of the two types of evidence has its own characteristics and obeys a belief update rule that is different from hard evidence, and different from each other. The particular emphasis is on belief update with multiple uncertain evidences. Efficient algorithms for BN reasoning with consistent and inconsistent uncertain evidences are developed, and their convergences analyzed. These algorithms can be seen as combining the techniques of traditional BN reasoning, Pearl's virtual evidence method, Jeffrey's rule, and the iterative proportional fitting procedure.
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MacGilchrist, Renaud S., and Julia Roloff. "A Bayesian Belief Network Exploring CSP Relationships." Academy of Management Proceedings 2015, no. 1 (January 2015): 16323. http://dx.doi.org/10.5465/ambpp.2015.16323abstract.

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Chaudhari, Santosh Kumar, and Hema A. Murthy. "ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM." ICTACT Journal on Communication Technology 02, no. 02 (June 1, 2011): 357–62. http://dx.doi.org/10.21917/ijct.2011.0049.

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Xiang, Y., Y. Tang, and W. Zhu. "Mobile sensor network noise reduction and recalibration using a Bayesian network." Atmospheric Measurement Techniques 9, no. 2 (February 4, 2016): 347–57. http://dx.doi.org/10.5194/amt-9-347-2016.

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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
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Dissertations / Theses on the topic "Bayesian belief network"

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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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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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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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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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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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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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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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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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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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Books on the topic "Bayesian belief network"

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

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

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Ibrahimovi, Semir, Nijaz Bajgori, and Lejla Turulja. Maximizing Information System Availability Through Bayesian Belief Network Approaches: Emerging Research and Opportunities. IGI Global, 2017.

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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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Applying Bayesian Belief Networks in Sun Tzu's Art of War. Storming Media, 2004.

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

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Strategic Economic Decisionmaking Using Bayesian Belief Networks To Solve Complex Problems. Springer-Verlag New York Inc., 2012.

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Book chapters on the topic "Bayesian belief network"

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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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Gran, Bjørn Axel, and Atte Helminen. "A Bayesian Belief Network for Reliability Assessment." In Computer Safety, Reliability and Security, 35–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45416-0_4.

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Cheng, Jie, and Russell Greiner. "Learning Bayesian Belief Network Classifiers: Algorithms and System." In Advances in Artificial Intelligence, 141–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45153-6_14.

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Blaser, Lilian, Matthias Ohrnberger, Carsten Riggelsen, and Frank Scherbaum. "Bayesian Belief Network for Tsunami Warning Decision Support." In Lecture Notes in Computer Science, 757–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02906-6_65.

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Taran, Viktoriya, and Tatyana Gubina. "Modeling of Complex Systems over Bayesian Belief Network." In Communications in Computer and Information Science, 192–202. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78273-3_19.

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Wongthanavasu, S. "A Bayesian Belief Network Model for Breast Cancer Diagnosis." In Operations Research Proceedings, 3–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20009-0_1.

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Intan, Rolly, and Oviliani Yenty Yuliana. "Fuzzy Bayesian Belief Network for Analyzing Medical Track Record." In Advances in Intelligent Information and Database Systems, 279–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12090-9_24.

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Mcheick, Hamid, Malak Khreiss, Hala Sweidan, and Iyad Zaarour. "PHEN: Parkinson Helper Emergency Notification System Using Bayesian Belief Network." In Lecture Notes in Business Information Processing, 212–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17957-5_14.

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Toropova, Aleksandra, and Tatiana Tulupyeva. "Comparison of Behavior Rate Models Based on Bayesian Belief Network." In Recent Research in Control Engineering and Decision Making, 510–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65283-8_42.

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Bharwad, Nileshkumar D., and Mukesh M. Goswami. "Classification for Multi-Relational Data Mining Using Bayesian Belief Network." In Smart Innovation, Systems and Technologies, 537–43. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_62.

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Conference papers on the topic "Bayesian belief network"

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Kondakci, Suleyman. "Network Security Risk Assessment Using Bayesian Belief Networks." In 2010 IEEE Second International Conference on Social Computing (SocialCom). IEEE, 2010. http://dx.doi.org/10.1109/socialcom.2010.141.

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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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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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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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Bashar, A., G. P. Parr, S. I. McClean, B. W. Scotney, M. Subramanian, S. K. Chaudhari, and T. A. Gonsalves. "Employing Bayesian Belief Networks for energy efficient Network Management." In 2010 National Conference On Communications (NCC). IEEE, 2010. http://dx.doi.org/10.1109/ncc.2010.5430172.

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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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Chen, Long, Heather T. Ma, Songsong Liu, Dezhang Yuan, and Xiaopeng Wang. "Posture estimation by Bayesian Network with Belief Propagation." In TENCON 2013 - 2013 IEEE Region 10 Conference. IEEE, 2013. http://dx.doi.org/10.1109/tencon.2013.6719001.

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Fam, Mei Ling, Dimitrios Konovessis, Xuhong He, Lin Seng Ong, and Hoon Kiang Tan. "Analysing Dependent Failures in a Bayesian Belief Network." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95853.

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Abstract Fault trees (FT) and event trees (ET) have been used thoroughly in risk analysis and there have been a few published articles outlining how to map FTs and ETs to Bayesian Belief Networks (BBN). There have been documented benefits of a BBN being able to consider Common Cause Failures (CCF) and conditional dependencies. With modelling CCFs in a BBN, there is a possibility to increase the level of analysis of a CCF by breaking down the analysis to the respective CCF Categories, such as Environment, Maintenance or Design. This allows a better understanding of the contributing events given a defined accident scenario. Also, in the decommissioning industry, there is no established database yet for CCF of components, as decommissioning projects are sparse and spread out across different operating conditions. Hence it may be practical to adjust generic CCFs to obtain facility-specific parameters for common cause failures. The paper thus highlights how to express CCFs with a Beta-Factor Model in a BBN and by extension, undertake an extended level of analysis according to CCF categories and adjust generic database common cause factors to a facility-specific factor based on a checklist. The technique is applied to a risk analysis of a well plugging and abandonment event.
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Rahimian, M. Amin, Ali Jadbabaie, and Elchanan Mossel. "Complexity of Bayesian belief exchange over a network." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264038.

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Reports on the topic "Bayesian belief network"

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Vlek, R. J., D. J. M. Willems, and H. Rijgersberg. Requirements for implementation : a quality prediction system for soft fruit based on a Bayesian Belief Network. Wageningen: Wageningen Food and Biobased Research, 2018. http://dx.doi.org/10.18174/563391.

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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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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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