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arXiv · 1206.3555

A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs

Andreas Stuhlmüller·Noah D. Goodman
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Abstract

We describe a dynamic programming algorithm for computing the marginal distribution of discrete probabilistic programs. This algorithm takes a functional interpreter for an arbitrary probabilistic programming language and turns it into an efficient marginalizer. Because direct caching of sub-distributions is impossible in the presence of recursion, we build a graph of dependencies between sub-distributions. This factored sum-product network makes (potentially cyclic) dependencies between subproblems explicit, and corresponds to a system of equations for the marginal distribution. We solve these equations by fixed-point iteration in topological order. We illustrate this algorithm on examples used in teaching probabilistic models, computational cognitive science research, and game theory.

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Andreas Stuhlmüller, Noah D. Goodman. 2012-09-10. A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. https://arxiv.org/abs/1206.3555

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