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Books on the topic 'Probabilistic Graphical Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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1

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

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2

Sucar, Luis Enrique. Probabilistic Graphical Models. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61943-5.

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3

van der Gaag, Linda C., and Ad J. Feelders, eds. Probabilistic Graphical Models. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0.

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4

Sucar, Luis Enrique. Probabilistic Graphical Models. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3.

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5

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

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6

Lucas, Peter, José A. Gámez, and Antonio Salmerón, eds. Advances in Probabilistic Graphical Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68996-6.

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7

Nir, Friedman, ed. Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press, 2010.

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8

Dechter, Rina. Reasoning with Probabilistic and Deterministic Graphical Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01583-0.

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9

Dechter, Rina. Reasoning with Probabilistic and Deterministic Graphical Models. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-01566-3.

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10

Dechter, Rina. Reasoning with probabilistic and deterministic graphical models: Exact algorithms. San Rafael, California]: Morgan & Claypool Publishers, 2013.

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11

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003189886.

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12

Studený, Milan. Probabilistic conditional independence structures. London: Springer, 2005.

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13

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

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14

Peter Lucas,Jos a. G. Mez,Antonio Salmer N. Advances in Probabilistic Graphical Models. Springer, 2008.

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15

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

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16

Sucar, Luis Enrique. Probabilistic Graphical Models: Principles and Applications. Springer International Publishing AG, 2020.

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17

Ji, Qiang. Probabilistic Graphical Models for Computer Vision. Elsevier Science & Technology Books, 2019.

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18

Sucar, Luis Enrique Enrique. Probabilistic Graphical Models: Principles and Applications. Springer, 2016.

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19

Koller, Daphne, Francis Bach, and Nir Friedman. Probabilistic Graphical Models: Principles and Tec. MIT Press, 2009.

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20

Probabilistic Graphical Models for Computer Vision. Elsevier, 2020. http://dx.doi.org/10.1016/c2014-0-04359-6.

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21

Koller, Daphne, and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.

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22

Sucar, Luis Enrique. Probabilistic Graphical Models: Principles and Applications. Springer International Publishing AG, 2021.

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23

Ji, Qiang. Probabilistic Graphical Models for Computer Vision. Elsevier Science & Technology, 2019.

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24

Sucar, Luis Enrique. Probabilistic Graphical Models: Principles and Applications. Springer, 2015.

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25

Koller, Daphne, and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.

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26

Sucar, Luis Enrique. Probabilistic Graphical Models: Principles and Applications. Springer London, Limited, 2015.

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27

Learning Probabilistic Graphical Models in R. Packt Publishing - ebooks Account, 2016.

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28

Building Probabilistic Graphical Models with Python. Packt Publishing, 2014.

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29

Ankan, Ankur, and Abinash Panda. Mastering Probabilistic Graphical Models using Python. Packt Publishing - ebooks Account, 2015.

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30

Sinoquet, Christine. Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics. Edited by Raphaël Mourad. Oxford University Press, 2014. http://dx.doi.org/10.1093/acprof:oso/9780198709022.001.0001.

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31

Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. Oxford University Press, 2014.

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32

Xiang, Yang. Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2010.

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33

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms. Morgan & Claypool Publishers, 2019.

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34

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms. Morgan & Claypool Publishers, 2019.

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35

Xiang, Yang. Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2009.

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36

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2002.

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37

Ankan, Ankur, and Abinash Panda. Mastering Probabilistic Graphical Models Using Python: Master Probabilistic Graphical Models by Learning Through Real-World Problems and Illustrative Code Examples in Python. Packt Publishing, Limited, 2015.

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38

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. Taylor & Francis Group, 2022.

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39

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. Taylor & Francis Group, 2022.

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40

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. CRC Press LLC, 2022.

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41

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. CRC Press LLC, 2022.

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42

Tran, Kim Phuc. Machine Learning and Probabilistic Graphical Models for Decision Support Systems. Taylor & Francis Group, 2022.

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43

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition. Morgan & Claypool Publishers, 2019.

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44

Sreedharan, Rina. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition. Springer International Publishing AG, 2019.

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45

(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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46

Hybrid Random Fields A Scalable Approach To Structure And Parameter Learning In Probabilistic Graphical Models. Springer, 2011.

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47

Studeny, Milan. Probabilistic Conditional Independence Structures. Springer London, Limited, 2006.

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48

Studeny, Milan. Probabilistic Conditional Independence Structures. Springer, 2010.

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49

Heunen, Chris, and Jamie Vicary. Categories for Quantum Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198739623.001.0001.

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Abstract:
Monoidal category theory serves as a powerful framework for describing logical aspects of quantum theory, giving an abstract language for parallel and sequential composition and a conceptual way to understand many high-level quantum phenomena. Here, we lay the foundations for this categorical quantum mechanics, with an emphasis on the graphical calculus that makes computation intuitive. We describe superposition and entanglement using biproducts and dual objects, and show how quantum teleportation can be studied abstractly using these structures. We investigate monoids, Frobenius structures and Hopf algebras, showing how they can be used to model classical information and complementary observables. We describe the CP construction, a categorical tool to describe probabilistic quantum systems. The last chapter introduces higher categories, surface diagrams and 2-Hilbert spaces, and shows how the language of duality in monoidal 2-categories can be used to reason about quantum protocols, including quantum teleportation and dense coding. Previous knowledge of linear algebra, quantum information or category theory would give an ideal background for studying this text, but it is not assumed, with essential background material given in a self-contained introductory chapter. Throughout the text, we point out links with many other areas, such as representation theory, topology, quantum algebra, knot theory and probability theory, and present nonstandard models including sets and relations. All results are stated rigorously and full proofs are given as far as possible, making this book an invaluable reference for modern techniques in quantum logic, with much of the material not available in any other textbook.
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50

Freno, Antonino, and Edmondo Trentin. Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models. Springer, 2011.

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