tuto08_csc5150

# tuto08_csc5150 - Latent variables -- a graphical...

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CSC5150 Learning Theory and Computational Finance Tutorial 08 By Tu Shikui Nov.06, 2007 Some slides are from Shi Lei 2006 tutorials.

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Outline Finite Mixture Models Gaussian Mixture Model (GMM) Mean Square Error (MSE) clustering analysis Expectation Maximization (EM) algorithm How EM works? An intuitive explanation
Finite Mixture Models

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Examples for Finite Mixture Model
EM for GMM

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Latent variables -- label for the data complete incomplete Complete and Incomplete Data

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Unformatted text preview: Latent variables -- a graphical representation EM in general How EM works? (1/)-- A decomposition q(z) is an arbitrary distribution of latent variable z Geometric representation of the decomposition How EM works? (2/) E-Step : max ln p ( x | θ ) min KL ( q || p ) Let q(z) = p(z|x,θ) q ( z ) M-Step : max L ( q , θ ) θ Intuitively … E-Step Intuitively … M-Step End of This Tutorial! Thank you!...
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## This note was uploaded on 10/13/2009 for the course CS 5150 taught by Professor Xulei during the Spring '09 term at University of Central Arkansas.

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tuto08_csc5150 - Latent variables -- a graphical...

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