Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
392605 | Information Sciences | 2014 | 12 Pages |
Abstract
We introduce a methodology for sensitivity analysis of evidence variables in Gaussian Bayesian networks. Knowledge of the posterior probability distribution of the target variable in a Bayesian network, given a set of evidence, is desirable. However, this evidence is not always determined; in fact, additional information might be requested to improve the solution in terms of reducing uncertainty. In this study we develop a procedure, based on Shannon entropy and information theory measures, that allows us to prioritize information according to its utility in yielding a better result. Some examples illustrate the concepts and methods introduced.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Miguel Angel Gómez-Villegas, Paloma Main, Paola Viviani,