Asymptotic model selection for naive Bayesian networks

被引：0

作者：

Rusakov, D ^{[1
]}

Geiger, D ^{[1
]}

机构：

[1] Technion Israel Inst Technol, Dept Comp Sci, IL-32000 Haifa, Israel

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2005年 / 6卷

关键词：

Bayesian networks; asymptotic model selection; Bayesian information criterion (BIC);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1 / 35

页数：35

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