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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| Vydáno v: | Proceedings of ACM on programming languages Ročník 9; číslo OOPSLA2; s. 220 - 247 |
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09.10.2025
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| ISSN: | 2475-1421, 2475-1421 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 280 |
| Author | Chen, Weiyi Dillig, Işıl Wang, Chao Halalingaiah, Shashin Wang, Jingbo |
| Author_xml | – sequence: 1 givenname: Jingbo orcidid: 0000-0001-5877-2677 surname: Wang fullname: Wang, Jingbo email: wang6203@purdue.edu organization: Purdue University, West Lafayette, USA – sequence: 2 givenname: Shashin orcidid: 0000-0002-1268-4345 surname: Halalingaiah fullname: Halalingaiah, Shashin email: shashin@cs.utexas.edu organization: University of Texas at Austin, Austin, USA – sequence: 3 givenname: Weiyi orcidid: 0009-0009-6276-3525 surname: Chen fullname: Chen, Weiyi email: chen5332@purdue.edu organization: Purdue University, West Lafayette, USA – sequence: 4 givenname: Chao orcidid: 0009-0003-4684-3943 surname: Wang fullname: Wang, Chao email: wang626@usc.edu organization: University of Southern California, Los Angeles, USA – sequence: 5 givenname: Işıl orcidid: 0000-0001-8006-1230 surname: Dillig fullname: Dillig, Işıl email: isil@cs.utexas.edu organization: University of Texas at Austin, Austin, USA |
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