2013/03/27 by Ben P. Wise, Wise, Ben P.
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1304.3116
openalex publication_date 2013/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper examines the biases and performance of several uncertain inference systems: Mycin, a variant of Mycin. and a simplified version of probability using conditional independence assumptions. We present axiomatic arguments for using Minimum Cross Entropy inference as the best way to do uncertain inference. For Mycin and its variant we found special situations where its performance was very good, but also situations where performance was worse than random guessing, or where data was interpreted as having the opposite of its true import We have found that all three of these systems usually gave accurate results, and that the conditional independence assumptions gave the most robust results. We illustrate how the Importance of biases may be quantitatively assessed and ranked. Considerations of robustness might be a critical factor is selecting UlS's for a given application.