Approximate counting, the Lovasz local lemma, and inference in graphical models
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In this paper we introduce a new approach for approximately counting in bounded degree systems with higher-order constraints. Our main result is an algorithm to approximately count the number of solutions to a CNF formula φ when the width is logarithmic in the maximum degree. This closes an exponential gap between the known upper and lower bounds. Moreover our algorithm extends straightforwardly to approximate sampling, which shows that under Lovász Local Lemma-like conditions it is not only possible to find a satisfying assignment, it is also possible to generate one approximately uniformly at random from the set of all satisfying assignments. Our approach is a significant departure from earlier techniques in approximate counting, and is based on a framework to bootstrap an oracle for computing marginal probabilities on individual variables. Finally, we give an application of our results to show that it is algorithmically possible to sample from the posterior distribution in an interesting class of graphical models.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mathematics
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing - STOC 2017
Association for Computing Machinery (ACM)
Moitra, Ankur. “Approximate Counting, the Lovasz Local Lemma, and Inference in Graphical Models.” Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing - STOC 2017 (2017).