Relevant bibliographies by topics / Applications of Bayesian networks

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

Academic literature on the topic 'Applications of Bayesian networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Applications of Bayesian networks"

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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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Aussem, Alex. "Bayesian networks." Neurocomputing 73, no. 4-6 (2010): 561–62. http://dx.doi.org/10.1016/j.neucom.2009.11.001.

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Verduijn, Marion, Niels Peek, Peter M. J. Rosseel, Evert de Jonge, and Bas A. J. M. de Mol. "Prognostic Bayesian networks." Journal of Biomedical Informatics 40, no. 6 (2007): 609–18. http://dx.doi.org/10.1016/j.jbi.2007.07.003.

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Verduijn, Marion, Peter M. J. Rosseel, Niels Peek, Evert de Jonge, and Bas A. J. M. de Mol. "Prognostic Bayesian networks." Journal of Biomedical Informatics 40, no. 6 (2007): 619–30. http://dx.doi.org/10.1016/j.jbi.2007.07.004.

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Heckerman, David, Abe Mamdani, and Michael P. Wellman. "Real-world applications of Bayesian networks." Communications of the ACM 38, no. 3 (1995): 24–26. http://dx.doi.org/10.1145/203330.203334.

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Zheng, Cui Fang, Long Jiang, Li Qing Jiang, and Zhi Jie Wu. "Application and Research of Bayesian Network in Data Mining." Advanced Materials Research 532-533 (June 2012): 738–42. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.738.

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Data mining techniques give us a feasible method to deal with great amount of data, which is generated during the software developing. Many methods have been used in data mining, Bayesian networks become a focus currently. It is a powerful tool and can be used to do uncertain inference. Bayesian networks have several advantages for data modeling. This paper mainly discusses the definition and building of Bayesian networks, research software engineer based on data mining, and builder a application model of data mining in software engineer, description detail the core arithmetic of Bayesian netw
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Kenett, Ron S. "Bayesian networks: Theory, applications and sensitivity issues." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630014. http://dx.doi.org/10.1142/s2425038416300147.

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This chapter is about an important tool in the data science workbench, Bayesian networks (BNs). Data science is about generating information from a given data set using applications of statistical methods. The quality of the information derived from data analysis is dependent on various dimensions, including the communication of results, the ability to translate results into actionable tasks and the capability to integrate various data sources [R. S. Kenett and G. Shmueli, On information quality, J. R. Stat. Soc. A 177(1), 3 (2014).] This paper demonstrates, with three examples, how the applic
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Canonne, Clement L., Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart. "Testing Bayesian Networks." IEEE Transactions on Information Theory 66, no. 5 (2020): 3132–70. http://dx.doi.org/10.1109/tit.2020.2971625.

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Bidyuk, B., and R. Dechter. "Cutset Sampling for Bayesian Networks." Journal of Artificial Intelligence Research 28 (January 28, 2007): 1–48. http://dx.doi.org/10.1613/jair.2149.

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The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, mor
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Mnatsakanyan, Z. R., H. S. Burkom, J. S. Coberly, and J. S. Lombardo. "Bayesian Information Fusion Networks for Biosurveillance Applications." Journal of the American Medical Informatics Association 16, no. 6 (2009): 855–63. http://dx.doi.org/10.1197/jamia.m2647.

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Dissertations / Theses on the topic "Applications of Bayesian networks"

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Wang, Jian. "Recovering Bayesian networks with applications to gene regulatory networks." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273725.

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Langseth, Helge. "Bayesian networks with applications in reliability analysis." Doctoral thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-959.

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<p>A common goal of the papers in this thesis is to propose, formalize and exemplify the use of Bayesian networks as a modelling tool in reliability analysis. The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and Paper V).</p><p><b>Paper I </b>is in this respect an application paper, where model building, estima
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Auld, Thomas James. "Bayesian applications of multilayer perceptron neural networks." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613209.

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Vogel, Kristin. "Applications of Bayesian networks in natural hazard assessments." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/6977/.

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Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments,
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Jaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.

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Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. Th
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Bashar, Abul. "On the application of Bayesian networks for autonomic network management." Thesis, Ulster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.646023.

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The quest for achieving an efficient, reliable and cost-effective network infrastructure in support of innovative and rich communication services has resulted in the advent and popularity of IP based converged Next Generation Networks (NGN). According to the ITU-T, the NGN has significant advantages such as support for end to end Quality of Service (QoS), generalised mobility, converged services between fixed & mobile networks and interworking with legacy networks. These networks require Network Management Systems (NMS), which play a key role in monitoring and administering them, to ensure smo
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Oteniya, Lloyd. "Bayesian belief networks for dementia diagnosis and other applications : a comparison of hand-crafting and construction using a novel data driven technique." Thesis, University of Stirling, 2008. http://hdl.handle.net/1893/497.

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The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common met
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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.
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Bang, Jung-Wook. "Hidden nodes in Bayesian networks and their application to prognostic analysis of hepatitis C." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271978.

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Hamilton, Benjamin Russell. "Applications of bayesian filtering in wireless networks: clock synchronization, localization, and rf tomography." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44707.

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In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a
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Books on the topic "Applications of Bayesian networks"

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E, Holmes Dawn, Jain L. C, and SpringerLink (Online service), eds. Innovations in Bayesian Networks: Theory and Applications. Springer-Verlag Berlin Heidelberg, 2008.

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Pourret, Olivier. Bayesian networks: A practical guide to applications. John Wiley, 2008.

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Nagarajan, Radhakrishnan. Bayesian Networks in R: With Applications in Systems Biology. Springer New York, 2013.

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Harrison, R. F. Neur al networks,heart attack and bayesian decisions: An application oof the Boltzmann perceptron network. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1994.

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Learning Bayesian networks. Prentice Hall, 2003.

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Neapolitan, Richard E. Learning Bayesian networks. Pearson Prentice Hall, 2004.

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M, Noble John, ed. Bayesian networks: An introduction. John Wiley & Sons, 2009.

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Koski, Timo. Bayesian networks: An introduction. Wiley, 2009.

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Koski, Timo. Bayesian networks: An introduction. John Wiley & Sons, 2009.

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Nagarajan, Radhakrishnan, Marco Scutari, and Sophie Lèbre. Bayesian Networks in R. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6446-4.

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Book chapters on the topic "Applications of Bayesian networks"

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Scutari, Marco, and Jean-Baptiste Denis. "Real-World Applications of Bayesian Networks." In Bayesian Networks, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429347436-8.

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Pinheiro Cinelli, Lucas, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, and Sérgio Lima Netto. "Bayesian Neural Networks." In Variational Methods for Machine Learning with Applications to Deep Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70679-1_4.

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Cano, Rafael, Carmen Sordo, and José M. Gutiérrez. "Applications of Bayesian Networks in Meteorology." In Advances in Bayesian Networks. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39879-0_17.

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Gómez, Manuel. "Real-World Applications of Influence Diagrams." In Advances in Bayesian Networks. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39879-0_9.

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de Campos, Luis M., Juan M. Fernández-Luna, and Juan F. Huete. "Fast Propagation Algorithms for Singly Connected Networks and their Applications to Information Retrieval." In Advances in Bayesian Networks. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39879-0_15.

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Lázaro, Marcelino, Francisco Herrera, and Aníbal R. Figueiras-Vidal. "Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks." In Engineering Applications of Neural Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23983-5_28.

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Wiegerinck, Wim, Willem Burgers, and Bert Kappen. "Bayesian Networks, Introduction and Practical Applications." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36657-4_12.

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Ito, Sosuke. "Bayesian Networks and Causal Networks." In Information Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1664-6_5.

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Maragoudakis, Manolis, Nikolaos K. Tselios, Nikolaos Fakotakis, and Nikolaos M. Avouris. "Improving SMS Usability Using Bayesian Networks." In Methods and Applications of Artificial Intelligence. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46014-4_17.

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Rudnianski, Michel, Utsav Sadana, and Hélène Bestougeff. "Bayesian Networks and Games of Deterrence." In Recent Advances in Game Theory and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43838-2_11.

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Conference papers on the topic "Applications of Bayesian networks"

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Sardeshmukh, Avadhut, Sreedhar Reddy, BP Gautham, and Amol Joshi. "Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.00-91.

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Peng, Qingsong. "Bayesian Networks for Data Prediction." In 2009 International Forum on Computer Science-Technology and Applications. IEEE, 2009. http://dx.doi.org/10.1109/ifcsta.2009.31.

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Kingsbury, Todd. "Architectures, algorithms, and applications using Bayesian networks." In SPIE Defense, Security, and Sensing, edited by Jerome J. Braun. SPIE, 2011. http://dx.doi.org/10.1117/12.882173.

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Buxton, H. "Advanced visual surveillance using Bayesian networks." In IEE Colloquium on Image Processing for Security Applications. IEE, 1997. http://dx.doi.org/10.1049/ic:19970385.

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Morshed, Nizamul, and Madhu Chetty. "Information Theoretic Dynamic Bayesian Network Approach for Reconstructing Genetic Networks." In Artificial Intelligence and Applications / Modelling, Identification, and Control. ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.717-079.

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Asamoah, Loretta Pinamang, Edwin Worlawoe Amaglo, and Quist-Aphetsi Kester. "Using Bayesian Networks In Analysing Judgement." In 2019 International Conference on Computing, Computational Modelling and Applications (ICCMA). IEEE, 2019. http://dx.doi.org/10.1109/iccma.2019.00025.

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Poo Kuan Hoong, Ong Kok Chien, I. K. T. Tan, and Choo-Yee Ting. "Road traffic prediction using Bayesian networks." In IET International Conference on Wireless Communications and Applications (ICWCA 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2098.

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Momani, Mohammad, Subhash Challa, and Rami Alhmouz. "BNWSN: Bayesian network trust model for wireless sensor networks." In 2008 Mosharaka International Conference on Communications, Computers and Applications (MIC-CCA 2008). IEEE, 2008. http://dx.doi.org/10.1109/miccca.2008.4669859.

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Azizzadenesheli, Kamyar, Emma Brunskill, and Animashree Anandkumar. "Efficient Exploration Through Bayesian Deep Q-Networks." In 2018 Information Theory and Applications Workshop (ITA). IEEE, 2018. http://dx.doi.org/10.1109/ita.2018.8503252.

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Kirsch and Kroschel. "Applying Bayesian networks to fault diagnosis." In Proceedings of IEEE International Conference on Control and Applications CCA-94. IEEE, 1994. http://dx.doi.org/10.1109/cca.1994.381203.

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Reports on the topic "Applications of Bayesian networks"

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Santos, Jr, and Eugene. Computing with Bayesian Multi-Networks. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273106.

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Acemoglu, Daron, Munther Dahleh, Ilan Lobel, and Asuman Ozdaglar. Bayesian Learning in Social Networks. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w14040.

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Schultz, Martin T., Thomas D. Borrowman, and Mitchell J. Small. Bayesian Networks for Modeling Dredging Decisions. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada552536.

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Chamberlain, Gary, and Guido Imbens. Nonparametric Applications of Bayesian Inference. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/t0200.

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Groeneveld, Andrew B., Stephanie G. Wood, and Edgardo Ruiz. Estimating Bridge Reliability by Using Bayesian Networks. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/39601.

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As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expe
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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Henderson, Thomas C., V. J. Mathews, and Dan Adams. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1004755.

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

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Roberts, Nancy A. Using Bayesian Networks and Decision Theory to Model Physical Security. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada411379.

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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), 2005. http://dx.doi.org/10.2172/875636.

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