Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10524491 | Journal of Multivariate Analysis | 2005 | 18 Pages |
Abstract
This paper presents a Bayesian decision theoretic foundation to the selection of a Bayesian network from data. We introduce the class of disintegrable loss functions to diversify the loss incurred in choosing different models. Disintegrable loss functions can iteratively be built from simple 0-L loss functions over pair-wise model comparisons and decompose the search for the model with minimum risk into a sequence of local searches, thus retaining the modularity of the model selection procedures for Bayesian networks.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Paola Sebastiani, Marco Ramoni,