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Algorithms and complexity results for #SAT and Bayesian inference

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3 Author(s)
Bacchus, F. ; Dept. of Comput. Sci., Toronto Univ., Ont., Canada ; Dalmao, S. ; Pitassi, T.

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 memorization (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.

Published in:

Foundations of Computer Science, 2003. Proceedings. 44th Annual IEEE Symposium on

Date of Conference:

11-14 Oct. 2003

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