Skip to content
conftrace_
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 - Statistical Learning
🧭 Keyword Pioneer - asymptotic analysis
🐣 Hot Topic Early Bird - bayesian network

Authors

Dmitry Rusakov, Dan Geiger

Topics

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

Keywords

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

More from JMLR 2005

All JMLR 2005 papers →
Dimension Reduction in Text Classification with Support Vector Machines
Stability of Randomized Learning Algorithms
Learning Hidden Variable Networks: The Information Bottleneck Approach
Diffusion Kernels on Statistical Manifolds
Information Bottleneck for Gaussian Variables
Multiclass Boosting for Weak Classifiers