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Composing inference algorithms as program transformations

2016-03-06 · Robert Zinkov, Chung-chieh Shan

Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to-program transformations. These transformations perform exact inference as well as generate probabilistic programs that compute expectations, densities, and MCMC samples. The resulting inference procedures are about as accurate and fast as other probabilistic programming systems on real-world problems.

📄 PDF Abstract BibTeX arXiv:1603.01882

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Code GenerationProbabilistic Programming

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