Empirical Bayes estimation utilizing finite Gaussian Mixture Models

Rafael Orellana, Rodrigo Carvajal, Juan C. Aguero

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728131856
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duración: 13 nov. 201927 nov. 2019

Serie de la publicación

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

Conferencia

Conferencia2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
País/TerritorioChile
CiudadValparaiso
Período13/11/1927/11/19

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