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.
Cite
Text
Rusakov and Geiger. "Asymptotic Model Selection for Naive Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2002.Markdown
[Rusakov and Geiger. "Asymptotic Model Selection for Naive Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2002.](https://mlanthology.org/uai/2002/rusakov2002uai-asymptotic/)BibTeX
@inproceedings{rusakov2002uai-asymptotic,
title = {{Asymptotic Model Selection for Naive Bayesian Networks}},
author = {Rusakov, Dmitry and Geiger, Dan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2002},
pages = {438-445},
url = {https://mlanthology.org/uai/2002/rusakov2002uai-asymptotic/}
}