Representing Bayesian Networks within Probabilistic Horn Abduction

2013/03/20 by David Poole, David L. Poole, Poole, David L.
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI

paper · pdf · doi:10.48550/arxiv.1303.5738

Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)

arxiv created 2013/03/20 · openalex publication_date 2013/03/20 · arxiv updated 2013/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a simple framework for Horn clause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and probabilistic notions of evidential reasoning. This can be used as a basis for a new way to implement Bayesian Networks that allows for approximations to the value of the posterior probabilities, and also points to a way that Bayesian networks can be extended beyond a propositional language.

Related

• Bayesian Logic Programs
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• A Probabilistic Reasoning Environment
• Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
• Probabilistic Inferences in Bayesian Networks