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Books on the topic 'Applications of Bayesian networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

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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2

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

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3

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

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4

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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5

Learning Bayesian networks. Prentice Hall, 2003.

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6

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

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7

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

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8

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

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9

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

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10

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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11

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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12

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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13

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

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14

Bayesian networks and decision graphs. Springer, 2001.

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15

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

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16

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

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17

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

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18

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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19

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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20

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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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

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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23

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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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

Koch, Karl-Rudolf. Bayesian inferencewith geodetic applications. Springer-Verlag, 1990.

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26

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

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27

Link, William A. Bayesian inference: With ecological applications. Academic Press/Elsevier, 2010.

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28

Koch, Karl-Rudolf. Bayesian Inference with Geodetic Applications. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0048699.

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29

Koch, Karl-Rudolf. Bayesian inference with geodetic applications. Springer-Verlag, 1990.

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30

Bayesian phylogenetics: Methods, algorithms, and applications. CRC Press/Taylor & Francis, 2014.

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31

Press, S. James. Bayesian statistics: Principles, models, and applications. Wiley, 1989.

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32

P, Tsokos Chris, ed. Bayesian theory and methods with applications. Atlantis Press, 2011.

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33

Savchuk, Vladimir, and Chris P. Tsokos. Bayesian Theory and Methods with Applications. Atlantis Press, 2011. http://dx.doi.org/10.2991/978-94-91216-14-5.

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34

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

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35

Approximation methods for efficient learning of Bayesian networks. IOS Press, 2008.

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36

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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37

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

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38

Fenton, Norman, and Martin Neil. Risk Assessment and Decision Analysis with Bayesian Networks. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b21982.

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39

Neil, Martin (Martin D.), ed. Risk assessment and decision analysis with Bayesian networks. Taylor & Francis, 2012.

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40

Fox, Jean-Paul. Bayesian item response modeling: Theory and applications. Springer, 2010.

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41

Current trends in Bayesian methodology with applications. CRC Press, 2015.

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42

Cypko, Mario A. Development of Clinical Decision Support Systems using Bayesian Networks. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-32594-7.

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43

Normand, Sharon-Lise Teresa *. Some statistical applications of Bayesian networks. 1990.

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44

McNair, Douglas, ed. Bayesian Networks - Advances and Novel Applications. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.75254.

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45

Bayesian Networks In R With Applications In Systems Biology. Springer-Verlag New York Inc., 2013.

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46

(Editor), Alexander Gammerman, J. G. Taylor (Editor), and V. Rayward-Smith (Editor), eds. Probabilistic Reasoning and Bayesian Belief Networks / Neural Networks / Applications of Modern Heuristic Methods. Alfred Waller, 1994.

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47

(Editor), Olivier Pourret, Patrick Naïm (Editor), and Bruce Marcot (Editor), eds. Bayesian Networks: A Practical Guide to Applications (Statistics in Practice). Wiley, 2008.

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48

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 discuss

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49

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

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50

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

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