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Books on the topic 'Bayesian network'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 June 2025

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1

Weber, Philippe, and Christophe Simon. Benefits of Bayesian Network Models. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119347316.

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2

Sridhar, Narasi, ed. Bayesian Network Modeling of Corrosion. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-56128-3.

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3

Ankush, Mittal, and Kassim Ashraf, eds. Bayesian network technologies: Applications and graphical models. Idea Group Pub., 2007.

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4

Das, Monidipa, and Soumya K. Ghosh. Enhanced Bayesian Network Models for Spatial Time Series Prediction. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27749-9.

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5

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

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6

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

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7

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

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8

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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9

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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10

Wang, Jun. A Bayesian classifier based on a deterministic annealing neural network for aircraft fault classification. Human Resources Directorate, Logistics Research Division, U.S. Air Force Armstrong Laboratory, 1997.

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11

University, Duke, and Geological Survey (U.S.), eds. Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach. U.S. Dept. of the Interior, U.S. Geological Suvery, 2012.

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12

Learning Bayesian networks. Prentice Hall, 2003.

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13

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

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14

Gámez, José A., Serafín Moral, and Antonio Salmerón, eds. Advances in Bayesian Networks. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39879-0.

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15

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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16

Holmes, Dawn E., and Lakhmi C. Jain, eds. Innovations in Bayesian Networks. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85066-3.

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17

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

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18

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

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19

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

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20

Neal, Radford M. Bayesian learning for neural networks. University of Toronto, Dept. of Computer Science, 1995.

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21

Jensen, Finn V., and Thomas D. Nielsen. Bayesian Networks and Decision Graphs. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-68282-2.

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22

Suzuki, Joe, and Maomi Ueno, eds. Advanced Methodologies for Bayesian Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28379-1.

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23

Cai, Baoping, Yonghong Liu, Zengkai Liu, Yuanjiang Chang, and Lei Jiang. Bayesian Networks for Reliability Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-6516-4.

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24

Neal, Radford M. Bayesian Learning for Neural Networks. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0745-0.

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25

Almond, Russell G., Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, and David M. Williamson. Bayesian Networks in Educational Assessment. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2125-6.

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26

Kjærulff, Uffe B., and Anders L. Madsen. Bayesian Networks and Influence Diagrams. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-74101-7.

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27

Jensen, Finn V. Bayesian Networks and Decision Graphs. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3502-4.

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28

Jensen, Finn V. An introduction to Bayesian networks. Springer, 1996.

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29

Bayesian networks and decision graphs. Springer, 2001.

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30

Jensen, Finn V. An introduction to Bayesian networks. UCL Press, 1996.

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31

Neal, Radford M. Bayesian learning for neural networks. Springer, 1996.

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32

Modeling and reasoning with Bayesian networks. Cambridge University Press, 2009.

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33

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

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34

E, Nicholson Ann, ed. Bayesian artificial intelligence. Chapman & Hall/CRC, 2004.

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35

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

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36

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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37

Rebai, Ahmed, ed. Bayesian Network. Sciyo, 2010. http://dx.doi.org/10.5772/258.

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38

Bayesian Network. Sciyo, 2010.

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39

Neapolitan, Richard, and Xia Jiang. The Bayesian Network Story. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.31.

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Abstract:

Bayesian networks are now among the leading architectures for reasoning with uncertainty in artificial intelligence. This chapter concerns their story, namely what they are, how and why they came into being, how we obtain them, and what they actually represent. First, it is shown that a standard application of Bayes’ Theorem constitutes inference in a two-node Bayesian network. Then more complex Bayesian networks are presented. Next the genesis of Bayesian networks and their relationship to causality is presented. A technique for learning Bayesian networks from data follows. Finally, a discussion of the philosophy of the probability distribution represented by a Bayesian network is provided.

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40

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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41

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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42

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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43

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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44

Woodberry, Owen, and Steven Mascaro. Programming Bayesian Network Solutions with Netica. Lulu Press, Inc., 2014.

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45

Bayesian network technologies: Applications and graphical models. Idea Group Pub., 2007.

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46

(Editor), Ankush Mittal, and Ashraf Kassim (Editor), eds. Bayesian Network Technologies: Applications and Graphical Models. IGI Publishing, 2007.

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47

Lee, Marcus T. H. A Bayesian neural network model of consumer choice. 2003.

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48

Detecting Fuzzy Duplicates in XML Data Using Bayesian Network. ASDF International, 2017.

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49

Mittelstadt, Daniel Richard. Application of a bayesian network to integrated circuit tester diagnosis. 1993.

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50

Shojima, Kojiro. Test Data Engineering: Latent Rank Analysis, Biclustering, and Bayesian Network. Springer Singapore Pte. Limited, 2022.

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