2013/03/27 by Richard E. Neapolitan, Neapolitan, Richard E., James R. Kenevan +1
Computer Science · Mathematics · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Probability and Statistical Research
paper · pdf · doi:10.48550/arxiv.1304.2369
openalex publication_date 2013/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When knowledge is obtained from a database, it is only possible to deduce confidence intervals for probability values. With confidence intervals replacing point values, the results in the set covering model include interval constraints for the probabilities of mutually exclusive and exhaustive explanations. The Principle of Interval Constraints ranks these explanations by determining the expected values of the probabilities based on distributions determined from the interval, constraints. This principle was developed using the Classical Approach to probability. This paper justifies the Principle of Interval Constraints with a more rigorous statement of the Classical Approach and by defending the concept of probabilities of probabilities.