James Cussens, University of York

 Model Selection using weighted MAX-SAT Solvers

This talk concerns encoding problems of statistical model selection in such a way that "weighted MAX-SAT solvers" can be used to search for the   'best' model. In this approach each model is (implicitly) encoded as a joint instantiation of n binary variables. Each of these binary variables encodes the truth/falsity of a logical proposition and weighted logical formulae are used to represent the model selection problem. Once encoded in this way we can tap into years of research and use any of the state-of-the-art solvers to conduct the search. In the talk I will show how to use this approach when the model class is that of Bayesian networks, and also for clustering. I will briefly touch on related methods which permit the calculation of marginal probabilities in discrete distributions.