44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings. (2003)
Oct. 11, 2003 to Oct. 14, 2003
Fahiem Bacchus , University of Toronto
Shannon Dalmao , University of Toronto
Toniann Pitassi , University of Toronto
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 proceed to show that there are instances where #DPLLCache can achieve an exponential speedup over existing algorithms.
T. Pitassi, F. Bacchus and S. Dalmao, "Algorithms and Complexity Results for #SAT and Bayesian Inference," 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings.(FOCS), Cambridge, Massachusettes, 2003, pp. 340.