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Books on the topic 'Probabilistic Bayesian Network'

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Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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1

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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2

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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3

Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470091754.

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4

Probabilistic methods for bionformatics: With an introduction to Bayesian networks. Burlington, MA: Morgan Kaufmann Publishers, 2009.

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5

Taroni, Franco, Alex Biedermann, Silvia Bozza, Paolo Garbolino, and Colin Aitken. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118914762.

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6

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

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7

Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. Wiley & Sons, Limited, John, 2006.

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8

Cowell, Robert G., David J. Spiegelhalter, Steffen L. Lauritzen, and Philip Dawid. Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks. Springer London, Limited, 2006.

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9

Neapolitan, Richard E. Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks. Elsevier Science & Technology Books, 2009.

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10

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

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11

(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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12

Taroni, Franco, Colin Aitken, Paolo Garbolino, Alex Biedermann, and Silvia Bozza. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Wiley & Sons, Incorporated, John, 2014.

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13

Taroni, Franco, Colin Aitken, Paolo Garbolino, Alex Biedermann, and Silvia Bozza. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Wiley & Sons, Incorporated, John, 2014.

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14

Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science (Statistics in Practice). Wiley, 2006.

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15

Taroni, Franco, Colin Aitken, Paolo Garbolino, Alex Biedermann, and Silvia Bozza. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Wiley & Sons, Limited, John, 2014.

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16

Cowell, Robert G., David J. Spiegelhalter, A. Philip Dawid, and Steffen L. Lauritzen. Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics). Springer, 2007.

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17

Lucas, Peter, José A. Gámez, and Antonio Salmerón Cerdan. Advances in Probabilistic Graphical Models. Springer London, Limited, 2007.

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18

Lucas, Peter, José A. Gámez, Various, and Antonio Salmerón Cerdan. Advances in Probabilistic Graphical Models. Springer, 2010.

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19

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science 2e Statistics in Practice. John Wiley and Sons Ltd, 2014.

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20

(Editor), Peter Lucas, José A. Gámez (Editor), and Antonio Salmerón (Editor), eds. Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing). Springer, 2007.

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21

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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22

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Abstract:
Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.
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