# Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

# Probabilistic Graphical Models

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_156-1

## 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 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...

## Keywords

Bayesian Network Markov Random Field Joint Probability Distribution Conditional Random Field Variable Node
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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## References

1. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco
2. 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

1. Bishop C (2006) Pattern recognition and machine learning. Springer, New York; has a chapter on graphical models which provides a good introduction
2. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge, MA; a detailed treatise on PGMs
3. Srihari S. Lecture slides and videos on machine learning and PGMs at http://www.cedar.buffalo.edu/~srihari/CSE574