2013/03/27 by Judea Pearl, Pearl, Judea
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.2716
Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The apparent failure of individual probabilistic expressions to distinguish uncertainty about truths from uncertainty about probabilistic assessments have prompted researchers to seek formalisms where the two types of uncertainties are given notational distinction. This paper demonstrates that the desired distinction is already a built-in feature of classical probabilistic models, thus, specialized notations are unnecessary.