Asymptotic Model Selection for Naive Bayesian Networks

Abstract

We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features. This formula deviates from the standard BIC score. Our work provides a concrete example that the BIC score is generally not valid for statistical models that belong to a stratified exponential family. This stands in contrast to linear and curved exponential families, where the BIC score has been proven to provide a correct approximation for the marginal likelihood.

Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)

Cited by in corpus (3)

• Stochastic complexity of Bayesian networks
• Effective Dimensions of Hierarchical Latent Class Models
• Algebraic Statistics in Model Selection