Experimentally Comparing Uncertain Inference Systems to Probability

03/27/2013 ∙ by Ben P. Wise, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 4

page 6

page 7

page 8

page 9

page 10

page 11

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.