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Bayesian inference is an important problem with numerous applications in probabilistic reasoning. Counting satisfying assignments is a closely related problem of fundamental theoretical importance. In this paper, we show that plain old DPLL equipped with memoization (an algorithm we call #DPLLCache) can solve both of these problems with time complexity that is at least as good as state-of-the-art exact algorithms, and that it can also achieve the best known time-space tradeoff. We then proceeddoi:10.1109/sfcs.2003.1238208 dblp:conf/focs/BacchusDP03 fatcat:3k44cr7nprbrdk2cpuf66j72em