In recent years, there has been considerable progress on fast randomized algorithms that ap-proximate probabilistic inference with tight toler-ance and confidence guarantees. The idea here is to formulate inference as a counting task over an annotated propositional theory, called weighted model counting (WMC), which can be parti-tioned into smaller tasks using universal hashing. An inherent limitation of this approach, how-ever, is that it only admits the inference of dis-crete probability distributions. In this work, we consider the problem of approximating inference tasks for a probability distribution defined over discrete and continuous random variables. Build-ing on a notion called weighted model integra-tion, which is a strict generalization of WMC and is based on annotating Boolean and arithmetic constraints, we show how probabilistic inference in hybrid domains can be put within reach of hashing-based WMC solvers. Empirical evalu-ations demonstrate the applicability and promise of the proposal. 1

@inproceedings{BelleUAI15,
  author    = {Belle, Vaishak and Van den Broeck, Guy and Passerini, Andrea},
  title     = {Hashing-Based Approximate Probabilistic Inference in Hybrid Domains},
  booktitle= {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2015},
  url={http://starai.cs.ucla.edu/papers/BelleUAI15.pdf},
  annotation = "(UAI best paper award)",
  keywords   = {conference,selective}
}