Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks

Authors:
Nikolai Slobodianik;Dmitry Zaporozhets;Neal Madras
Affiliations:
-;-;-
Venue:
The Journal of Machine Learning Research
Year:
2009

Citing 4
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Optimal structure identification with greedy search

The Journal of Machine Learning Research
Learning Bayesian Networks

Learning Bayesian Networks
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)

The Minimum Description Length Principle (Adaptive Computation and Machine Learning)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the machine learning community, the Bayesian scoring criterion is widely used for model selection problems. One of the fundamental theoretical properties justifying the usage of the Bayesian scoring criterion is its consistency. In this paper we refine this property for the case of binomial Bayesian network models. As a by-product of our derivations we establish strong consistency and obtain the law of iterated logarithm for the Bayesian scoring criterion.