Empirical Bayes estimation utilizing finite Gaussian Mixture Models

Rafael Orellana, Rodrigo Carvajal, Juan C. Aguero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this paper we develop an identification algorithm to obtain an estimation of the prior distribution in the classical problem of Bayesian inference. We consider the Empirical Bayes approach to obtain the prior distribution approximation by a finite Gaussian mixture. An Expectation-Maximization based algorithm is used to obtain an estimate of the Gaussian mixture parameters. Our approach shows a good approximation of the prior distribution when the number of experiments is increased. We illustrate the estimation performance of our proposal with numerical simulations.

Original languageEnglish
Title of host publicationIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131856
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duration: 13 Nov 201927 Nov 2019

Publication series

NameIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019

Conference

Conference2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
Country/TerritoryChile
CityValparaiso
Period13/11/1927/11/19

Keywords

  • Bayesian inference
  • Empirical Bayes
  • ExpectationMaximization
  • Gaussian Mixture
  • Prior distribution

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