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Probabilistic Graphical Models

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Encyclopedia of Social Network Analysis and Mining

Synonyms

Bayesian networks; Markov networks; Markov random fields

Bayesian network (BN):

A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables.

Conditional probability distribution:

Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known.

Conditional random field (CRF):

A partially directed graph that represents a conditional distribution.

Factor graph:

A type of parameterization of PGMs in the form of bipartite graphs of factor nodes and variable nodes, where a factor node indicates that the variable nodes is connected to form a clique in a PGM.

Graph:

A set of nodes and edges, where edges connect pairs of nodes.

Inference:

Process of answering queries using the distribution as the model of the world.

Joint probability distribution:

Assignment of probabilities to...

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References

  • Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco

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  • Wasserman S, Faust K (1994) Social network analysis in the social and behavioral sciences. In: Social network analysis: methods and applications. Cambridge University Press, Cambridge, p 127

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Recommended Reading

  • Bishop C (2006) Pattern recognition and machine learning. Springer, New York; has a chapter on graphical models which provides a good introduction

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  • Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge, MA; a detailed treatise on PGMs

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  • Srihari S. Lecture slides and videos on machine learning and PGMs at http://www.cedar.buffalo.edu/~srihari/CSE574

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Acknowledgments

The author wishes to thank his teaching and research assistants for the PGM course (CSE 674 at the University at Buffalo). In particular, Dmitry Kovalenko, Yingbo Zhao, Chang Su, and Yu Liu for many discussions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Sargur N. Srihari

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  1. Sargur N. Srihari
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Correspondence to Sargur N. Srihari .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. American University of Sharjah, Sharjah, United Arab Emirates

    Suheil Khoury

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Srihari, S.N. (2017). Probabilistic Graphical Models. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_156-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_156-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

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