Proceedings

Automated Safety Verification of Posterior Distributions of Probabilistic Programs

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 807-816.

https://doi.org/10.24963/ijcai.2026/91

Abstract

Ensuring the safety of probabilistic systems is a central challenge in formal verification.

We propose an automated refinement technique for verifying a safety property of posterior distributions in probabilistic programs.

Our approach builds on the counterexample-guided abstraction refinement (CEGAR) framework and exploits the duality in convex optimisation and the adequacy of predicate-transformer semantics in probabilistic settings.

We implement the technique and evaluate its effectiveness through preliminary experiments.

Kazuki Watanabe, and Hiroshi Unno. 2026. Automated Safety Verification of Posterior Distributions of Probabilistic Programs. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 807–816. https://doi.org/10.24963/ijcai.2026/91
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