Modular probabilistic programming with algebraic effects (MSc Thesis 2019)
Oliver Goldstein, Ohad Kammar

TL;DR
This thesis introduces Koka Bayes, a modular probabilistic programming library embedded in Koka, leveraging algebraic effects to improve the composability and correctness of inference algorithms.
Contribution
It presents a novel modular probabilistic programming library using algebraic effects, bridging the gap between theory and practice in Bayesian inference.
Findings
Koka Bayes enables modular composition of inference algorithms.
The library is validated semantically within the Koka language.
Algebraic effects model fundamental probabilistic operations effectively.
Abstract
Probabilistic programming languages, which exist in abundance, are languages that allow users to calculate probability distributions defined by probabilistic programs, by using inference algorithms. However, the underlying inference algorithms are not implemented in a modular fashion, though, the algorithms are presented as a composition of other inference components. This discordance between the theory and the practice of Bayesian machine learning, means that reasoning about the correctness of probabilistic programs is more difficult, and composing inference algorithms together in code may not necessarily produce correct compound inference algorithms. In this dissertation, I create a modular probabilistic programming library, already a nice property as its not a standalone language, called Koka Bayes, that is based off of both the modular design of Monad Bayes -- a probabilistic…
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Taxonomy
TopicsOptimization and Mathematical Programming · Process Optimization and Integration · AI-based Problem Solving and Planning
