Verifying Sampling Algorithms via Distributional Invariants

Fuente: arXiv
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Main Authors: Zilken, Daniel, Batz, Kevin, Katoen, Joost-Pieter, Winkler, Tobias
Format: Preprint
Published: 2025
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author Zilken, Daniel
Batz, Kevin
Katoen, Joost-Pieter
Winkler, Tobias
author_facet Zilken, Daniel
Batz, Kevin
Katoen, Joost-Pieter
Winkler, Tobias
contents This paper presents a Hoare-like veri cation framework for discrete probabilistic programs that we apply to two non-trivial sampling algorithms: Lumbroso's Fast Dice Roller and Saad et al.'s Fast Loaded Dice Roller. These algorithms have previously resisted formal veri cation due to their probabilistic nature, intricate loop structure, and parametric input. Our approach complements existing proof rules based on inductive distributional invariants, enabling us to verify both total and partial correctness of the two algorithms.
format Preprint
id arxiv_https___arxiv_org_abs_2509_06410
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Verifying Sampling Algorithms via Distributional Invariants
Zilken, Daniel
Batz, Kevin
Katoen, Joost-Pieter
Winkler, Tobias
Logic in Computer Science
Discrete Mathematics
This paper presents a Hoare-like veri cation framework for discrete probabilistic programs that we apply to two non-trivial sampling algorithms: Lumbroso's Fast Dice Roller and Saad et al.'s Fast Loaded Dice Roller. These algorithms have previously resisted formal veri cation due to their probabilistic nature, intricate loop structure, and parametric input. Our approach complements existing proof rules based on inductive distributional invariants, enabling us to verify both total and partial correctness of the two algorithms.
title Verifying Sampling Algorithms via Distributional Invariants
topic Logic in Computer Science
Discrete Mathematics
url https://arxiv.org/abs/2509.06410