Uncertainty in Artificial Intelligence
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Asymptotic Model Selection for Naive Bayesian Networks
Dmitry Rusakov, Dan Geiger
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.
Pages: 438-445
PS Link:
PDF Link: /papers/02/p438-rusakov.pdf
AUTHOR = "Dmitry Rusakov and Dan Geiger",
TITLE = "Asymptotic Model Selection for Naive Bayesian Networks",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "438--445"

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