2020/01/08 by David Tolpin, Tolpin, David, Tomer Dobkin +1
Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Computer science #Decision-Making and Behavioral Economics #FOS: Computer and information sciences #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Probabilistic CTL #Probabilistic analysis of algorithms #Probabilistic logic #Probabilistic relevance model #Programming Languages (cs.PL) #Statistical Methods and Bayesian Inference #Statistics #Stochastic modelling #Theoretical computer science #cs.LG #cs.PL #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.02656
published in arXiv (Cornell University) (Cornell University) · 7 pages main body, 4 pages appendix
openalex publication_date 2020/01/08 · arxiv created 2020/01/22 · arxiv updated 2020/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them. Stochastic probabilistic programs allow straightforward specification and efficient inference in models with nuisance parameters, noise, and nondeterminism. We give several examples of stochastic probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference. We conclude with discussion of open research topics and related work.