Compositional Stochastic Modeling and Probabilistic Programming

Eric Mjolsness

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arXiv · 1212.0582

Compositional Stochastic Modeling and Probabilistic Programming

Eric Mjolsness

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Abstract

Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in parallel (and possibly interacting) have summed time-evolution operators. From this foundation, algorithms for simulation, inference and model reduction may be systematically derived. The useful consequences are potentially far-reaching in computational science, machine learning and beyond. Hybrid compositional stochastic modeling/probabilistic programming approaches may also be possible.

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Eric Mjolsness. 2012-12-03. Compositional Stochastic Modeling and Probabilistic Programming. https://arxiv.org/abs/1212.0582

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