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 one-size-fi...
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| Vydané v: | Proceedings of ACM on programming languages Ročník 4; číslo OOPSLA; s. 1 - 31 |
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| Jazyk: | English |
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13.11.2020
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| ISSN: | 2475-1421, 2475-1421 |
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| Abstract | 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 one-size-fits 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 domain-specific 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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| AbstractList | 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 one-size-fits 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 domain-specific 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. 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 one-size-fits 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 domain-specific 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. |
| ArticleNumber | 140 |
| Author | Van den Broeck, Guy Holtzen, Steven Millstein, Todd |
| Author_xml | – sequence: 1 givenname: Steven surname: Holtzen fullname: Holtzen, Steven email: sholtzen@cs.ucla.edu organization: University of California at Los Angeles, USA – sequence: 2 givenname: Guy surname: Van den Broeck fullname: Van den Broeck, Guy email: guyvdb@cs.ucla.edu organization: University of California at Los Angeles, USA – sequence: 3 givenname: Todd surname: Millstein fullname: Millstein, Todd email: todd@cs.ucla.edu organization: University of California at Los Angeles, USA |
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(e_1_2_2_38_1) Katz Jonathan (e_1_2_2_50_1) 1996 e_1_2_2_82_1 e_1_2_2_80_1 Zeng Zhe (e_1_2_2_85_1) 2020 Holtzen Steven (e_1_2_2_44_1) 2020 Clarke Edmund M. (e_1_2_2_21_1) 1999 Gorinova Maria I (e_1_2_2_40_1) 2020 e_1_2_2_37_1 e_1_2_2_39_1 Petrus Kwisthout Johan Henri (e_1_2_2_57_1) e_1_2_2_10_1 e_1_2_2_52_1 e_1_2_2_31_1 e_1_2_2_54_1 e_1_2_2_73_1 e_1_2_2_18_1 e_1_2_2_33_1 e_1_2_2_56_1 e_1_2_2_79_1 e_1_2_2_35_1 e_1_2_2_58_1 e_1_2_2_77_1 Onisko Agnieszka (e_1_2_2_67_1) Zhou Yuan (e_1_2_2_86_1) 2020 Chaganty Arun (e_1_2_2_15_1) 2013 |
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| Snippet | Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of... |
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| SubjectTerms | Mathematics of computing Probabilistic inference problems Probabilistic representations Probability and statistics |
| SubjectTermsDisplay | Mathematics of computing -- Probability and statistics -- Probabilistic inference problems Mathematics of computing -- Probability and statistics -- Probabilistic representations |
| Title | Scaling exact inference for discrete probabilistic programs |
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