Probabilistic Programming: A Program Analysis Perspective

Date: Wednesday, June 5, 2013
Speaker: Sriram Rajamani
Venue: IST Austria

Probabilistic models, particularly those with causal dependencies, can be succinctly written as probabilistic programs. Recent years have seen a proliferation of languages for writing such probabilistic programs, as well as tools and techniques for performing inference over these programs. In this talk, we show that static and dynamic program analysis techniques can be used to infer quantities of interest to the machine learning community, thereby providing a new and interesting domain of application for program analysis. In particular, we show that static analysis techniques inspired by dataflow analysis and iterative refinement techniques can be used for Bayesian inference. We also show that dynamic analysis techniques inspired by directed testing and symbolic execution can be used for sampling probabilistic programs.

Posted in RiSE Seminar