Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures⁎

Rafael Orellana; Rodrigo Carvajal; Juan C. Agüero

doi:10.1016/j.ifacol.2018.09.200

Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures^⁎

Rafael Orellana, Rodrigo Carvajal, Juan C. Agüero

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	706-711
Number of pages	6
Journal	18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018
Volume	51
Issue number	15
DOIs	https://doi.org/10.1016/j.ifacol.2018.09.200
State	Published - 1 Jan 2018
Externally published	Yes

Keywords

Estimation
Expectation-Maximization
Gaussian Mixture
Maximum Likelihood
Optimization

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10.1016/j.ifacol.2018.09.200

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abstract = "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.",

keywords = "Estimation, Expectation-Maximization, Gaussian Mixture, Maximum Likelihood, Optimization",

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Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures^⁎ . / Orellana, Rafael; Carvajal, Rodrigo; Agüero, Juan C.
In: 18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018, Vol. 51, No. 15, 01.01.2018, p. 706-711.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures⁎

AU - Orellana, Rafael

AU - Carvajal, Rodrigo

AU - Agüero, Juan C.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Estimation

KW - Expectation-Maximization

KW - Gaussian Mixture

KW - Maximum Likelihood

KW - Optimization

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DO - 10.1016/j.ifacol.2018.09.200

M3 - Article

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SN - 2405-8963

VL - 51

SP - 706

EP - 711

JO - 18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018

JF - 18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018

IS - 15

ER -

Maximum Likelihood Infinite Mixture Distribution Estimation Utilizing Finite Gaussian Mixtures^⁎

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Keywords

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