Abstract:
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 achieves the best known time-space tradeoff. Our DPLL based algorithms have the potential to acheive better average-case performance than known algorithms on problems which possess additional structure. Probabilistic models of real situations tend to have such additional structure.