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 incorrect for statistical models that belong to stratified exponential families. This claim stands in contrast to linear and curved exponential families, where the BIC score has been proven to provide a correct asymptotic approximation for the marginal likelihood.
Cite
Text
Rusakov and Geiger. "Asymptotic Model Selection for Naive Bayesian Networks." Journal of Machine Learning Research, 2005.Markdown
[Rusakov and Geiger. "Asymptotic Model Selection for Naive Bayesian Networks." Journal of Machine Learning Research, 2005.](https://mlanthology.org/jmlr/2005/rusakov2005jmlr-asymptotic/)BibTeX
@article{rusakov2005jmlr-asymptotic,
title = {{Asymptotic Model Selection for Naive Bayesian Networks}},
author = {Rusakov, Dmitry and Geiger, Dan},
journal = {Journal of Machine Learning Research},
year = {2005},
pages = {1-35},
volume = {6},
url = {https://mlanthology.org/jmlr/2005/rusakov2005jmlr-asymptotic/}
}