Structure Variability in Bayesian Networks
Abstract
The structure of a Bayesian network encodes most of the information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study the variability of its network structure, which can be used to compare the performance of different learning algorithms and to measure the strength of any arbitrary subset of arcs. In this paper we will introduce some descriptive statistics and the corresponding parametric and Monte Carlo tests on the undirected graph underlying the structure of a Bayesian network, modeled as a multivariate Bernoulli random variable.
 Publication:

arXiv eprints
 Pub Date:
 September 2009
 DOI:
 10.48550/arXiv.0909.1685
 arXiv:
 arXiv:0909.1685
 Bibcode:
 2009arXiv0909.1685S
 Keywords:

 Statistics  Methodology;
 Mathematics  Statistics Theory;
 Statistics  Machine Learning
 EPrint:
 21 pages, 4 figures