Asymptotic Model Selection for Naive Bayesian Networks

2012/12/12 by Dmitry Rusakov, Rusakov, Dmitry, Dan Geiger +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.1301.0598

openalex publication_date 2012/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Stochastic complexity of Bayesian networks

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