Asymptotic model selection for directed networks with hidden variables

Asymptotic model selection for directed networks with hidden variables (1996)

by Dan Geiger , David Heckerman , Christopher Meek

Citations:

37 - 11 self

Datum	Value	Source
TITLE	Asymptotic Model Selection for Directed Networks with Hidden Variables ∗	SVM HeaderParse 0.2
AUTHOR NAME	Dan Geiger	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science Department	SVM HeaderParse 0.2
AUTHOR ADDR	Technion, Haifa 32000, Israel	SVM HeaderParse 0.2
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research, Bldg 9S	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond WA, 98052-6399	SVM HeaderParse 0.2
AUTHOR NAME	Christopher Meek	SVM HeaderParse 0.2
AUTHOR AFFIL	Carnegie-Mellon University; Department of Philosophy	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
CITATIONS	23 found	ParsCit 1.0