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Swift: Compiled Inference for Probabilistic Programming Languages
TL;DR: Swift is described, a compiler for the BLOG PPL that incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes.
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Abstract: A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.
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Citations
•Proceedings Article
Deep Probabilistic Programming
Dustin Tran,Matthew D. Hoffman,Rif A. Saurous,Eugene Brevdo,Kevin Murphy,David M. Blei +5 more
- 13 Jan 2017
TL;DR: Edward, a Turing-complete probabilistic programming language, is proposed, which makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC.
Compiling Markov chain Monte Carlo algorithms for probabilistic modeling
Daniel Huang,Jean-Baptiste Tristan,Greg Morrisett +2 more
- 14 Jun 2017
TL;DR: A compiler is described that transforms a probabilistic model written in a restricted modeling language and a query for posterior samples given observed data into a Markov Chain Monte Carlo (MCMC) inference algorithm that implements the query.
36
AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms for Probabilistic Models
Subho S. Banerjee,Zbigniew Kalbarczyk,Ravishankar K. Iyer +2 more
- 04 Apr 2019
TL;DR: AcMC2 is presented, a compiler that transforms PMs into optimized hardware accelerators (for use in FPGAs or ASICs) that utilize Markov chain Monte Carlo methods to infer and query a distribution of posterior samples from the model.
24
Incremental inference for probabilistic programs
Marco F. Cusumano-Towner,Benjamin Bichsel,Timon Gehr,Martin Vechev,Vikash K. Mansinghka +4 more
- 11 Jun 2018
TL;DR: This work introduces the concept of a trace translator which adapts samples from P into samples of Q, and presents this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by Q.
16
A design proposal for Gen: probabilistic programming with fast custom inference via code generation
Marco F. Cusumano-Towner,Vikash K. Mansinghka +1 more
- 18 Jun 2018
TL;DR: A design for a probabilistic programming language called Gen, embedded in Julia, that aims to be sufficiently expressive and performant for general-purpose use is proposed, and it is shown that Gen is more expressive than Stan, a widely used language for hierarchical Bayesian modeling.
9
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