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Probabilistic Graphical Models

  • Reference work entry
  • First Online:
Encyclopedia of Social Network Analysis and Mining

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Synonyms

Bayesian networks; Markov networks; Markov random fields

Glossary

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 node 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 :

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References

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

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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/New York, pp 1–27

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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

    MATH  Google Scholar 

  • Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT, Cambridge; 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.

Author information

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

Authors
  1. Sargur N. Srihari
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

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Srihari, S.N. (2014). Probabilistic Graphical Models. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_156

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