@inproceedings{c070296af6b44414b6cd5a6e3b12a3cb,
title = "Empirical Bayes estimation utilizing finite Gaussian Mixture Models",
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.",
keywords = "Bayesian inference, Empirical Bayes, ExpectationMaximization, Gaussian Mixture, Prior distribution",
author = "Rafael Orellana and Rodrigo Carvajal and Aguero, {Juan C.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 ; Conference date: 13-11-2019 Through 27-11-2019",
year = "2019",
month = nov,
doi = "10.1109/CHILECON47746.2019.8987584",
language = "English",
series = "IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019",
}