A Compilation Target for Probabilistic Programming Languages

Abstract

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.

In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014

References in corpus (2)

• On the role of interaction in sequential Monte Carlo algorithms
• Monolingual Probabilistic Programming Using Generalized Coroutines

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