Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures

Title: Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures
Creator: Orellana, Rafael; Carvajal, Rodrigo; Agüero, Juan C.
Relation: 18th IFAC Symposium on System Identification SYSID 2018. Proceedings of the 18th IFAC Symposium on System Identification SYSID 2018, Volume 51 (Stockholm, Sweden 09-11 July, 2018) p. 706-711
Publisher Link: http://dx.doi.org/10.1016/j.ifacol.2018.09.200
Publisher: International Federation of Automatic Control
Resource Type: conference paper
Date: 2018
Description: In this paper we develop a Maximum Likelihood estimation algorithm for the estimation of infinite mixture distributions. We assume a known conditional distribution, whilst the weighting distribution is assumed unknown and it is approximated by a finite Gaussian mixture. Our approach allows for the correct estimation of the Gaussian mixture parameters. We illustrate the estimation performance of our proposal with numerical simulations.
Subject: Gaussian mixture; estimation; maximum likelihood; expectation-maximization; optimization
Identifier: http://hdl.handle.net/1959.13/1447925
Identifier: uon:43263
Language: eng
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