Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network

Authors:
David Maxwell Chickering;David Heckerman
Affiliations:
Computer Science Department, University of California, Los Angeles, CA and Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA
Venue:
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Year:
1996

Citing 7
Cited 7

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Bayesian classification (AutoClass): theory and results

Advances in knowledge discovery and data mining
A Guide to the Literature on Learning Probabilistic Networks from Data

IEEE Transactions on Knowledge and Data Engineering
Operations for learning with graphical models

Journal of Artificial Intelligence Research
Local learning in probabilistic networks with hidden variables

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Asymptotic model selection for directed networks with hidden variables*

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Finding overlapping components with MML

Statistics and Computing
Efficient Bayesian inference for harmonic models via adaptive posterior factorization

Neurocomputing
A multivariate discretization method for learning Bayesian networks from mixed data

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Score and information for recursive exponential models with incomplete data

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. We consider the Laplace approximation and the less accurate but more efficient BIC/MDL approximation. We also consider approximations proposed by Draper (1993) and Cheeseman and Stutz (1995). These approximations are as efficient as BIC/MDL, but their accuracy has not been studied in any depth. We compare the accuracy of these approximations under the assumption that the Laplace approximation is the most accurate. In experiments using synthetic data generated from discrete naive-Bayes models having a hidden root node, we find that (1) the BIC/MDL measure is the least accurate, having a bias in favor of simple models, and (2) the Draper and CS measures are the most accurate, having a bias in favor of simple and complex models, respectively.