An efficient method for computing a Bayesian mixture of feature-based models

A. Jalalirad, T.J. Tjalkens

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Abstract

We describe a computationally efficient method to produce a specific Bayesian mixture of all the models in a finite set of feature-based models that assign a probability to the observed data set. Special attention is given to the bound on the regret of using the mixture instead of the best model in the set. It is proven theoretically and verified through synthetic data that this bound is relatively tight. Comparing the workload of the proposed method with the direct implementation of the Bayesian mixture shows an almost exponential improvement of computing time.
Original languageEnglish
Article number1651002
Number of pages26
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume30
Issue number1
DOIs
Publication statusPublished - 2016

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