Asymptotic Model Selection for Directed Networks with Hidden Variables View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Dan Geiger , David Heckerman , Christopher Meek

ABSTRACT

We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian network with hidden variables is the rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node. This manuscript was previously published in The Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, 1996, Morgan Kaufmann. More... »

PAGES

461-477

Book

TITLE

Learning in Graphical Models

ISBN

978-94-010-6104-9
978-94-011-5014-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-011-5014-9_16

DOI

http://dx.doi.org/10.1007/978-94-011-5014-9_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033861074


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