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2005 JMLR JMLR 2005

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. [abs] [ pdf ][ bib ] © JMLR 2005. (edit, beta)

📈 Trend Setter — Bayesian Inference
🧭 Keyword Pioneer — marginal likelihood
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
🐣 Hot Topic Early Bird — model selection

Authors

Dmitry Rusakov , Dan Geiger

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Optimization & Theory > Theory Machine Learning > Bayesian & Probabilistic > Bayesian Inference

Keywords

model selection marginal likelihood asymptotic analysis bayesian network asymptotic approximation bic score stratified exponential family
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