Asymptotic Model Selection for Naive Bayesian Networks

Dmitry Rusakov; Dan Geiger

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arXiv · 1301.0598

Asymptotic Model Selection for Naive Bayesian Networks

Dmitry Rusakov·Dan Geiger

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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.

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Dmitry Rusakov, Dan Geiger. 2012-12-12. Asymptotic Model Selection for Naive Bayesian Networks. https://arxiv.org/abs/1301.0598

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