Probabilistic Inference for Datalog with Correlated Inputs

Probabilistic extensions of logic programming languages, such as ProbLog, integrate logical reasoning with probabilistic inference to evaluate probabilities of output relations; however, prior work does not account for potential statistical correlations among input facts. This paper introduces Prali...

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Veröffentlicht in:Proceedings of ACM on programming languages Jg. 9; H. OOPSLA2; S. 220 - 247
Hauptverfasser: Wang, Jingbo, Halalingaiah, Shashin, Chen, Weiyi, Wang, Chao, Dillig, Işıl
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 09.10.2025
Schlagworte:
Constraint and logic programming
Mathematical optimization
Mathematics of computing
Probabilistic inference problems
Theory of computation
probabilistic logic programming
constrained optimization
type inference
ISSN:2475-1421, 2475-1421
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Zusammenfassung:Probabilistic extensions of logic programming languages, such as ProbLog, integrate logical reasoning with probabilistic inference to evaluate probabilities of output relations; however, prior work does not account for potential statistical correlations among input facts. This paper introduces Praline, a new extension to Datalog designed for precise probabilistic inference in the presence of (partially known) input correlations. We formulate the inference task as a constrained optimization problem, where the solution yields sound and precise probability bounds for output facts. However, due to the complexity of the resulting optimization problem, this approach alone often does not scale to large programs. To address scalability, we propose a more efficient δ-exact inference algorithm that leverages constraint solving, static analysis, and iterative refinement. Our empirical evaluation on challenging real-world benchmarks, including side-channel analysis, demonstrates that our method not only scales effectively but also delivers tight probability bounds.
ISSN:2475-1421
2475-1421
DOI:10.1145/3763058