Informative Priors for Markov Blanket Discovery

We present a novel interpretation of information theoretic feature selection as optimization of a discriminative model. We show that this formulation coincides with a group of mutual information based filter heuristics in the literature, and show how our probabilistic framework gives a well-founded extension for informative priors. We then derive a particular sparsity prior that recovers the well-known IAMB algorithm (Tsamardinos & Aliferis, 2003) and extend it to create a novel algorithm, IAMB-IP, that includes domain knowledge priors. In empirical evaluations, we find the new algorithm to improve Markov Blanket recovery even when a misspecified prior was used, in which half the prior knowledge was incorrect.