
Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for...
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Conditional independence by typing
A central goal of probabilistic programming languages (PPLs) is to separ...
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Exact Symbolic Inference in Probabilistic Programs via SumProduct Representations
We present the SumProduct Probabilistic Language (SPPL), a new system t...
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Generating Functions for Probabilistic Programs
This paper investigates the usage of generating functions (GFs) encoding...
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Paradoxes of Probabilistic Programming
Probabilistic programming languages allow programmers to write down cond...
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A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
We describe a dynamic programming algorithm for computing the marginal d...
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Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
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Scaling Exact Inference for Discrete Probabilistic Programs
Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no onesizefits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are discrete. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs. We develop a domainspecific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allow Dice to scale to large probabilistic programs.
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