Bombay

The recent surge of interest in reasoning about probabilistic graphical models has led to the de-velopment of various techniques for probabilistic reasoning. Of these, techniques based on weighted model counting are particularly interesting since they can potentially leverage recent advances in un-weighted model counting and in propositional sat-isfiability solving. In this paper, we present a new approach to weighted model counting via reduc-tion to unweighted model counting. Our reduction, which is polynomial-time and preserves the normal form (CNF/DNF) of the input formula, allows us to exploit advances in unweighted model counting to solve weighted model counting instances. Exper-iments with weighted model counters built using our reduction indicate that these counters performs much better than a state-of-the-art weighted model counter.