Efficient Inference for Complex Queries on Complex Distributions


Lili Dworkin, Michael Kearns, Lirong Xia ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:211-219, 2014.


We consider problems of approximate inference in which the query of interest is given by a complex formula (such as a formula in disjunctive formal form (DNF)) over a joint distribution given by a graphical model. We give a general reduction showing that (approximate) marginal inference for a class of distributions yields approximate inference for DNF queries, and extend our techniques to accommodate even more complex queries, and dense graphical models with variational inference, under certain conditions. Our results unify and generalize classical inference techniques (which are generally restricted to simple marginal queries) and approximate counting methods such as those introduced by Karp, Luby and Madras (which are generally restricted to product distributions).

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