2013/03/27 by Richard E. Neapolitan, Neapolitan, Richard E., James R. Kenevan +2
Computer Science · #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.1304.1105
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The causal (belief) network is a well-known graphical structure for representing independencies in a joint probability distribution. The exact methods and the approximation methods, which perform probabilistic inference in causal networks, often treat the conditional probabilities which are stored in the network as certain values. However, if one takes either a subjectivistic or a limiting frequency approach to probability, one can never be certain of probability values. An algorithm for probabilistic inference should not only be capable of reporting the inferred probabilities; it should also be capable of reporting the uncertainty in these probabilities relative to the uncertainty in the probabilities which are stored in the network. In section 2 of this paper a method is given for determining the prior variances of the probabilities of all the nodes. Section 3 contains an approximation method for determining the variances in inferred probabilities.