Probabilistic Inference by Hashing and Optimization

Probabilistic inference is one of the central problems of statistical machine learning. To date, only a handful of distinct methods have been developed, most notably Monte Carlo sampling, decomposition, and variational methods. In this chapter, a different approach based on random projections and optimization is introduced.