Automatic Differentiation Equipped Variable Elimination for Sensitivity Analysis on Probabilistic Inference Queries

Druce, J.1, Ruttenberg, B.1, Blumstein, D.1, and Scofield, D.2

Presented at the Conference on Neural Information Processing Systems (NIPS), Long Beach, CA (December 2017)

Probabilistic Models are a natural framework for describing the stochastic relationships between variables in a system to perform inference tasks, such as estimating the probability of a specific set of conditions or events. In application, it is often appropriate to perform sensitivity analysis on a model, for example, to assess the stability of analytical results with respect to the governing parameters. However, typical programming language are cumbersome for encoding and reasoning with complex models and current approaches to sensitivity analysis on probabilistic models are not scalable, as they require repeated computation or estimation of the derivatives of complex functions. To overcome these limitations, and to enable efficient sensitivity analysis with respect to arbitrary model queries, e.g., P(X|Y=y), we propose to use Automatic Differentiation to extend the Probabilistic Programming Language Figaro.

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