this is for holding javascript data
Olga edited GMM.tex
over 9 years ago
Commit id: ba68493dfabe8ada4e704fa7fcabb40aeed94d6b
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diff --git a/GMM.tex b/GMM.tex
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$T^{(t)}_{j,i}=P(Z_i=j|X_i=x_i,\theta^{(t)})=\frac{P(z_{j})f(x_i|\mu^{(t)}_{j}, \sigma^{2(t)}_{j})}{p^{(t)} f(x_i|\mu^{(t)}_{1}, \sigma^{2(t)}_{1})+(1-p^{(t)})f(x_i|\mu^{(t)}_{2}, \sigma^{2(t)}_{2})}$
$Q(\mathbf{\theta}|\mathbf{\theta^{(t)}})=E_{\textbf{z}|\textbf{x},\mathbf{\theta^{(t)}}}(\log L(\mathbf{\theta};\textbf{x};\textbf{z})) =
E[( \log \prod \limits_{i=1}^n L(\mathbf{\theta};x_{i};z_{i})] = \sum\limits_{i=1}^n E[( \log L(\mathbf{\theta};x_{i};z_{i})]
= =$
$= \sum\limits_{i=1}^n \sum\limits_{j=1}^2 T^{(t)}_{j,i}[\log P(z_{j}) -\frac{1}{2}\log(2\pi) - \frac{1}{2}\log\sigma^{2}_{j} - \frac{(x_{i}-\mu_{j})^2}{2\sigma^{2}_{j}}]$
\textit{M-step}