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Books on the topic 'Bayesian intelligence'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

E, Nicholson Ann, ed. Bayesian artificial intelligence. 2nd ed. CRC Press, 2011.

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2

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

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3

Research Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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4

Dowe, David L., ed. Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44958-1.

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5

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

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6

Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2011.

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7

Szeliski, Richard. Bayesian Modeling of Uncertainty in Low-Level Vision. Springer US, 1989.

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8

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

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9

Williamson, Jon. Bayesian nets and causality: Philosophical and computational foundations. Oxford University Press, 2005.

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10

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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11

Sucar, L. Enrique, Eduardo F. Morales, and Jesse Hoey. Decision theory models for applications in artificial intelligence: Concepts and solutions. Information Science Reference, 2011.

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12

Erickson, Gary J. Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis. Springer Netherlands, 1998.

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13

Heidbreder, Glenn R. Maximum Entropy and Bayesian Methods: Santa Barbara, California, U.S.A., 1993. Springer Netherlands, 1996.

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14

Tulupʹev, A. L. Algebraicheskie baĭesovskie seti: Teoreticheskie osnovy i neprotivorechivostʹ. RAN, SPIIA, 1995.

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15

Kjaerulff, Uffe B. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. 2nd ed. Springer New York, 2013.

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16

Kjaerulff, Uffe B. Bayesian networks and influence diagrams: A guide to construction and analysis. Springer, 2008.

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17

Kjaerulff, Uffe B. Bayesian networks and influence diagrams: A guide to construction and analysis. Springer, 2008.

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18

Guy, Tatiana V. Decision Making and Imperfection. Springer Berlin Heidelberg, 2013.

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19

Corfield, David. Foundations of Bayesianism. Springer Netherlands, 2001.

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20

Portinale, Luigi. Modeling and analysis of dependable systems: A probabilistic graphical model perspective. World Scientific, 2015.

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21

School, Pardee Rand Graduate, ed. Finding needles in a haystack: A resource allocation methodology to design strategies to detect terrorist weapon development. RAND, 2009.

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22

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

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23

MACKAY, DAVID J. C. INFORMATION THEORY, INFERENCE, AND LEARNING ALGORITHMS. CAMBRIDGE UNIV PRESS, 2003.

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24

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2010.

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25

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2010.

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26

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. CRC Press, 2010. http://dx.doi.org/10.1201/b10391.

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27

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203491294.

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28

Korb, Kevin. Bayesian Artificial Intelligence. Taylor & Francis Group, 2003.

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29

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2003.

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30

Bayesian Artificial Intelligence. Taylor & Francis Group, 2023.

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31

Utilizing Bayesian Techniques for User Interface Intelligence. Storming Media, 1996.

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32

Dowe, David L. Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer, 2013.

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33

Neal, Radford M. Bayesian Learning for Neural Networks. Springer London, Limited, 2012.

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34

Williamson, Jon. Bayesian Nets and Causality: Philosophical and Computational Foundations. Ebsco Publishing, 2004.

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35

Williamson, Jon. Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford University Press, 2005.

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36

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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37

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence (Chapman & Hall/Crc Computer Science and Data Analysis). Chapman & Hall/CRC, 2003.

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38

Mislevy, Robert J., Russell G. Almond, Linda S. Steinberg, Duanli Yan, and David M. Williamson. Bayesian Networks in Educational Assessment. Springer, 2015.

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39

Mislevy, Robert J., Russell G. Almond, Linda S. Steinberg, Duanli Yan, and David M. Williamson. Bayesian Networks in Educational Assessment. Springer New York, 2015.

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40

Mislevy, Robert J., David Williamson, Linda Steinberg, Russell G. Almond, and Duanli Yan. Bayesian Networks in Educational Assessment. Springer New York, 2016.

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41

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

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42

Cheeseman, Peter. Bayesian Learning (The Stanford Computer Science Video Journal : Artificial Intelligence Research Lectures). Morgan Kaufmann Pub, 1993.

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43

Nielsen, Thomas D., and Finn V. Jensen. Bayesian Networks and Decision Graphs. Springer New York, 2010.

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44

Bayesian networks and decision graphs. Springer, 2001.

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45

Dowe, David L. Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 -- December 2 2011. Springer, 2013.

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46

Ling, Xiaoning. Dependent evidence in resoning with uncertainty. 1990.

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47

Nikolaev, Nikolay, and Hitoshi Iba. Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods. Springer, 2006.

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48

Wolpert, David, Tatiana Valentine Guy, and Miroslav Kárný. Decision Making with Imperfect Decision Makers. Springer, 2016.

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49

Wolpert, David, Tatiana Valentine Guy, and Miroslav Kárný. Decision Making with Imperfect Decision Makers. Springer, 2012.

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50

Káý, Miroslav, David Wolpert, and Tatiana Valentine Guy. Decision Making with Imperfect Decision Makers. Springer, 2011.

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