BayesL: Towards a Logical Framework for Bayesian Networks

Stefano M. Nicoletti; Mariëlle Stoelinga

doi:10.48550/arXiv.2506.23773

BayesL: Towards a Logical Framework for Bayesian Networks

Research output: Working paper › Preprint › Academic

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Abstract

We introduce BayesL, a novel logical framework for specifying, querying, and verifying the behaviour of Bayesian networks (BNs). BayesL (pronounced "Basil") is a structured language that allows for the creation of queries over BNs. It facilitates versatile reasoning concerning causal and evidence-based relationships, and permits comprehensive what-if scenario evaluations without the need for manual modifications to the model.

Original language	English
Publisher	ArXiv.org
DOIs	https://doi.org/10.48550/arXiv.2506.23773
Publication status	Published - 30 Jun 2025

Keywords

cs.AI
cs.LO

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10.48550/arXiv.2506.23773

2506.23773v1Final published version, 646 KB

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BayesL: Towards a Logical Framework for Bayesian Networks

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Keywords

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