The EM algorithm for general Gaussian model with latent variables

Marijus  Radavičius

doi:10.15388/LMR.2010.76

Articles

Marijus Radavičius

Matematikos ir informatikos institutas

Published 2010-12-21

https://doi.org/10.15388/LMR.2010.76

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Keywords

EM algorithm
Gaussian regression
nonlinear transform
maximum likelihood estimator

How to Cite

Radavičius, M. (2010) “The EM algorithm for general Gaussian model with latent variables”, Lietuvos matematikos rinkinys, 51(proc. LMS), pp. 420–425. doi:10.15388/LMR.2010.76.

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Abstract

The problem of estimating parameters of Gaussian vector when only its (nonlinear) transformation is observed is addressed. The EM algorithm equations to calculate maximum likelihood estimator are derived. In particular, closed-form formulas of the EM algorithm are derived in the case when only the minimum of two endogenous variables satisfying Gaussian regression model is observed.

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