Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs
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Abstract (arXiv)
We present a novel sampling framework for probabilistic programs. The framework combines two recent ideas -- control-data separation and logical condition propagation -- in a nontrivial manner so that the two ideas boost the benefits of each other. We implemented our algorithm on top of Anglican. The experimental results demonstrate our algorithm's efficiency, especially for programs with while loops and rare observations.
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References · 14
- 2019Introduction to Multi-Armed Banditscited by 475
- 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
- 2018An Introduction to Probabilistic Programmingcited by 62
- 2015Simple regret for infinitely many armed banditscited by 39
- 2016Inference Compilation and Universal Probabilistic Programmingcited by 31
- 2014Venture: a higher-order probabilistic programming platform with programmable inferencecited by 20
- 2016Design and Implementation of Probabilistic Programming Language Anglicancited by 15
- 2016Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraintscited by 12
- 2018Automated learning with a probabilistic programming language: Birchcited by 11
- 2019Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Supportcited by 11
- 2017Lexicographic Ranking Supermartingales: An Efficient Approach to Termination of Probabilistic Programscited by 5
- 2015Conditioning in Probabilistic Programmingcited by 3
2 more
- 2018Inference Trees: Adaptive Inference with Explorationcited by 3
- 2019Deployable probabilistic programmingcited by 3
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