Bayesian inference and counting satisfying assignments are important
problems with numerous applications in probabilistic reasoning. In this
paper, we show that plain old DPLL equipped with memoization can solve
both of these problems with time complexity that is at least as
good as all known algorithms. Furthermore, DPLL with memoization
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In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models. This reduction leads to a fully polynomial randomized approximation scheme (FPRAS) for ... more >>>