Testing Implication of Probabilistic Dependencies

2013/02/13 by Michael S. K. M. Wong, Wong, Michael S. K. M.
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Quality and Management #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.1302.3610

openalex publication_date 2013/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Axiomatization has been widely used for testing logical implications. This paper suggests a non-axiomatic method, the chase, to test if a new dependency follows from a given set of probabilistic dependencies. Although the chase computation may require exponential time in some cases, this technique is a powerful tool for establishing nontrivial theoretical results. More importantly, this approach provides valuable insight into the intriguing connection between relational databases and probabilistic reasoning systems.

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