Abstract
AbstractWe study discrete probabilistic programs with potentially unbounded looping behaviors over an infinite state space. We present, to the best of our knowledge, the first decidability result for the problem of determining whether such a program generates exactly a specified distribution over its outputs (provided the program terminates almost-surely). The class of distributions that can be specified in our formalism consists of standard distributions (geometric, uniform, etc.) and finite convolutions thereof. Our method relies on representing these (possibly infinite-support) distributions as probability generating functions which admit effective arithmetic operations. We have automated our techniques in a tool called $$\textsc {Prodigy}$$
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, which supports automatic invariance checking, compositional reasoning of nested loops, and efficient queries to the output distribution, as demonstrated by experiments.
Publisher
Springer International Publishing
Cited by
7 articles.
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1. A Completeness Theorem for Probabilistic Regular Expressions;Proceedings of the 39th Annual ACM/IEEE Symposium on Logic in Computer Science;2024-07-08
2. Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions;Proceedings of the ACM on Programming Languages;2024-04-29
3. Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures;Proceedings of the ACM on Programming Languages;2024-01-05
4. Distributional Probabilistic Model Checking;Lecture Notes in Computer Science;2024
5. Lower Bounds for Possibly Divergent Probabilistic Programs;Proceedings of the ACM on Programming Languages;2023-04-06