Gaussian Mixture Model

Lecture Note

Gaussian Distribution

The density can be estimated by multiples of Gaussian Kernels

$\pi$ is prior, not likelihood.
The mixing coefficients are themselves probabilities and must meet this condition: sum(pi)=1

How to find the optimal parameter $\Theta$ ?

Lets use the Maximum Likelihood Estimation to find the optimal parameter. This can be solved by E-M algorithm/

see PPT slides: VTech

The Hundred-Page Machine Learning Book http://themlbook.com/wiki/doku.php

머신러닝/패턴인식, 오일석

Last updated 3 years ago

Gaussian Distribution

The density can be estimated by multiples of Gaussian Kernels

$\pi$ is prior, not likelihood.
The mixing coefficients are themselves probabilities and must meet this condition: sum(pi)=1

How to find the optimal parameter $\Theta$ ?

Lets use the Maximum Likelihood Estimation to find the optimal parameter. This can be solved by E-M algorithm/

see PPT slides: VTech

The Hundred-Page Machine Learning Book http://themlbook.com/wiki/doku.php

머신러닝/패턴인식, 오일석