Learning in Graphical Gaussian Models

Paolo Giudici

doi:10.1093/oso/9780198523567.003.0040

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Learning in Graphical Gaussian Models

Paolo Giudici

Year: 1996 Pages: 621-628

DOI: 10.1093/oso/9780198523567.003.0040

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Abstract

Abstract We address the problem of statistical learning in a graphical Gaussian model from a Bayesian viewpoint. Two classes of prior distributions on the variance-covariance matrix which parameterises the model are introduced and compared. The implications on graphical model selection are discussed.

Keywords:

Graphical model Gaussian Computer science Model selection Variance (accounting) Selection (genetic algorithm) Bayesian probability Artificial intelligence Covariance Statistical graphics Machine learning Covariance matrix Statistical model Pattern recognition (psychology) Mathematics Algorithm Statistics Graphics Physics Computer graphics (images)

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Bayesian Modeling and Causal Inference

Physical Sciences → Computer Science → Artificial Intelligence

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

Learning in Graphical Gaussian Models

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