NDLI: Practical probabilistic programming with monads

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Practical probabilistic programming with monads

Content Provider	ACM Digital Library
Author	Ghahramani, Zoubin Ścibior, Adam Gordon, Andrew D.
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. .
Starting Page	165
Ending Page	176
Page Count	12
File Format	PDF
ISBN	9781450338080
DOI	10.1145/2804302.2804317
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Monads Bayesian statistics Probabilistic programming Monte carlo Haskell
Content Type	Text
Resource Type	Article

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