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Compositional Stochastic Modeling and Probabilistic Programming by Mjolsness, Eric is a scholarly article available to read on EtoBox.

What is Compositional Stochastic Modeling and Probabilistic Programming about?

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.

Author

Mjolsness, Eric

Published

2012

Language

EN