Author or contributor

Practical Probabilistic Programming with Monads

Resource type
Authors/contributors
Title
Practical Probabilistic Programming with Monads
Abstract
The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms, but they all express probabilistic models as computational processes using syntax resembling programming languages. In the functional programming community monads are known to offer a convenient and elegant abstraction for programming with probability distributions, but their use is often limited to very simple inference problems. We show that it is possible to use the monad abstraction to construct probabilistic models for machine learning, while still offering good performance of inference in challenging models. We use a GADT as an underlying representation of a probability distribution and apply Sequential Monte Carlo-based methods to achieve efficient inference. We define a formal semantics via measure theory. We demonstrate a clean and elegant implementation that achieves performance comparable with Anglican, a state-of-the-art probabilistic programming system.
Date
2015
Proceedings Title
Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell
Place
New York, NY, USA
Publisher
ACM
Pages
165–176
Series
Haskell '15
DOI
10/gft39z
ISBN
978-1-4503-3808-0
Accessed
2019-11-26T20:11:53Z
Library Catalog
ACM Digital Library
Extra
ZSCC: 0000048 event-place: Vancouver, BC, Canada
Citation
Ścibior, A., Ghahramani, Z., & Gordon, A. D. (2015). Practical Probabilistic Programming with Monads. In Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell (pp. 165–176). New York, NY, USA: ACM. https://doi.org/10/gft39z
PROBABILITY & STATISTICS
Methodology
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