Reviews: Approximate Knowledge Compilation by Online Collapsed Importance Sampling

The paper proposes an approach for approximate inference in discrete graphical models that generalizes collapsed importance sampling. The apprach is online, in the sense that the choice of variables to sample depends on the values already sampled, instead of being fixed. Moreover, the exact inference portion of the method is performed by knwoledge compilation so the approach is called collapsed compilation and allows approximate knowledge compilation. The method begins by multiplying factors obtaining a Sentential Decision Diagram. When the SDD becomes too large, a variable is deterministically chosen, a value for it is sampled from a proposal distribution that is the marginal of the variable in the current SDD and the SDD is conditioned on the sampled value.