Probabilistic reasoning for analysis of approximate computations

Popular application domains such as multimedia processing, machine learning, and big-data analytics operate on inherently noisy data and make decisions under uncertainty. While these applications are often good candidates for both algorithmic and system-level approximation, a major open challenge is how to analyze the influence of noisy data and candidate approximations on the application's outputs.

At the same time, probabilistic programming languages provide an intuitive way to model uncertainty by expressing complex probabilistic models as computer programs. The talk will give an overview of PSI (http://www.psisolver.org), a system for exact symbolic inference. PSI computes succinct symbolic representations of the joint posterior distribution represented by a probabilistic program using static analysis. PSI supports programs with both discrete and continuous distributions. It can compute answers to various posterior distribution queries, expectation queries and assertion queries using its own back-end for symbolic reasoning. This talk will present how we can represent some problems in approximate computing as probabilistic programs and use PSI to automatically get symbolic expressions that represent the distributions of the output error.