Asymptotic Model Selection for Naive Bayesian Networks
Artificial Intelligence
2013-01-07 v1 Machine Learning
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
@article{arxiv.1301.0598,
title = {Asymptotic Model Selection for Naive Bayesian Networks},
author = {Dmitry Rusakov and Dan Geiger},
journal= {arXiv preprint arXiv:1301.0598},
year = {2013}
}
Comments
Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)