Work

Compositional Stochastic Modeling and Probabilistic Programming

Year: 2012

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1212.0582

Author: Mjolsness, Eric

Institution: University of California, Irvine

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: California Institute of Technology, University of Cambridge, Eidgenössische Technische Hochschule Zürich, National Institutes of Health, University of California, Irvine, Los Alamos National Laboratory

OpenAlex ID: W1534037438

Cites: 17

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Related to: 20