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Operations for Learning with Graphical Models (1994)
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| Venue: | Journal of Artificial Intelligence Research |
| Citations: | 275 - 13 self |
BibTeX
@ARTICLE{Buntine94operationsfor,
author = {Wray L. Buntine},
title = {Operations for Learning with Graphical Models},
journal = {Journal of Artificial Intelligence Research},
year = {1994},
volume = {2},
pages = {159--225}
}
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Abstract
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
Keyphrases
graphical model graphical model perspective multidisciplinary review exponential family data analysis gibbs sampling linear regression paper conclu markov chain well-known example markov field statistical learning discrete bayesian network expectation maximization algorithm graphical framework popular algorithm undirected network probability model bayesian network graphical operation standard algorithm schema empirical learning graphical specification feed-forward network
