Rule-based Evolutionary Bayesian Learning

In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowledge and intuition. The resulting metho...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Botsas, Themistoklis, Mason, Lachlan R, Matar, Omar K, Pan, Indranil
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.02.2022
Subjects
Bayesian analysis
Equivalence
Evolution
Genetic algorithms
Machine learning
Statistical inference
Uncertainty
Online AccessGet full text

Cover

More Information
Summary:In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowledge and intuition. The resulting method creates a penalty equivalent to a common Bayesian prior, but it also includes information that typically would not be available within a standard Bayesian context. In this work, we extend the aforementioned methodology with grammatical evolution, a symbolic genetic programming technique that we utilise for automating the rules' derivation. Our motivation is that grammatical evolution can potentially detect patterns from the data with valuable information, equivalent to that of expert knowledge. We illustrate the use of the rule-based Evolutionary Bayesian learning technique by applying it to synthetic as well as real data, and examine the results in terms of point predictions and associated uncertainty.
ISSN:2331-8422