Bayesian Network Structure Learning with Permutation Tests

Abstract

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this paper we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.

13 pages, 4 figures. Presented at the Conference 'Statistics for Complex Problems', Padova, June 15, 2010

References in corpus (2)

• Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks
• Regularized estimation of large-scale gene association networks using graphical Gaussian models

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