What is it about?
In the probabilistic programming paradigm, Bayesian models are specified as programs and posterior inference is automated. This should make probabilistic modelling more accessible, however, it comes with new challenges. This work develops a framework to implement static analyses that help the practitioner fix problems specific to the probabilistic environment. As there is a multitude of probabilistic programming languages available, this frameworks allows a language-agnostic formulation of these analyses, maximising their area of application.
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Why is it important?
The barrier of entry to probabilistic programming is high for novices due to the domain-specific program errors. Enhancing the development experience with static analyses may lower this barrier.
Perspectives
Even with my background in mathematics and statistics, it took a long time to get the hang of probabilistic programming. My personal goal is to unlock the power of probabilistic programming for novices in Bayesian statistics.
Markus Boeck
Vienna University of Technology
Read the Original
This page is a summary of: Language-Agnostic Static Analysis of Probabilistic Programs, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3691620.3695031.
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