We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previ...
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
We present a near-optimal reduction from approximately counting the cardinality of a discrete set to approximately sampling elements of the set. An important application of our wo...
We introduce ApproxCount, an algorithm that approximates the number of satisfying assignments or models of a formula in propositional logic. Many AI tasks, such as calculating degr...
Asbestos-related illnesses become a nationwide problem in Japan. Now human inspectors check whether asbestos is contained in building material or not. To judge whether the specimen...