Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models

Robert G. Cowell

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arXiv · 1301.2262

Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models

Robert G. Cowell

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Abstract

It is often stated in papers tackling the task of inferring Bayesian network structures from data that there are these two distinct approaches: (i) Apply conditional independence tests when testing for the presence or otherwise of edges; (ii) Search the model space using a scoring metric. Here I argue that for complete data and a given node ordering this division is a myth, by showing that cross entropy methods for checking conditional independence are mathematically identical to methods based upon discriminating between models by their overall goodness-of-fit logarithmic scores.

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Robert G. Cowell. 2013-01-10. Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models. https://arxiv.org/abs/1301.2262

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