The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.
 Project name
|
<html><img src="/images/9/94/Report.png" border="0" /></html> Main project page
<html><img src="/images/c/c3/Resultset_previous.png" border="0" /></html>Previous entry<html> </html>Next entry<html><img src="/images/5/5c/Resultset_next.png" border="0" /></html>
|
Variational Bayes approach for the mixture of Normals
- Motivation: I have described on another page the basics of mixture models and the EM algorithm in a frequentist context. It is worth reading before continuing. Here I am interested in the Bayesian approach as well as in a specific variational method (nicknamed "Variational Bayes").
- Data: we have N univariate observations, [math]\displaystyle{ y_1, \ldots, y_N }[/math], gathered into the vector [math]\displaystyle{ \mathbf{y} }[/math].
- Assumptions: we assume the observations to be exchangeable and distributed according to a mixture of K Normal distributions. The parameters of this model are the mixture weights ([math]\displaystyle{ w_k }[/math]), the means ([math]\displaystyle{ \mu_k }[/math]) and the precisions ([math]\displaystyle{ \tau_k }[/math]) of each mixture components, all gathered into [math]\displaystyle{ \Theta = \{w_1,\ldots,w_K,\mu_1,\ldots,\mu_K,\tau_1,\ldots,\tau_K\} }[/math]. There are two constraints: [math]\displaystyle{ \sum_{k=1}^K w_k = 1 }[/math] and [math]\displaystyle{ \forall k \; w_k \gt 0 }[/math].
- Observed likelihood: [math]\displaystyle{ p(\mathbf{y} | \Theta, K) = \prod_{n=1}^N p(y_n|\Theta,K) = \prod_{n=1}^N \sum_{k=1}^K w_k Normal(y_n;\mu_k,\tau_k^{-1}) }[/math]
- Latent variables: let's introduce N latent variables, [math]\displaystyle{ z_1,\ldots,z_N }[/math], gathered into the vector [math]\displaystyle{ \mathbf{z} }[/math]. Each [math]\displaystyle{ z_n }[/math] is a vector of length K with a single 1 indicating the component to which the [math]\displaystyle{ n^{th} }[/math] observation belongs, and K-1 zeroes.
- Augmented likelihood: [math]\displaystyle{ p(\mathbf{y},\mathbf{z}|\Theta,K) = \prod_{n=1}^N p(y_n,z_n|\Theta,K) = \prod_{n=1}^N p(z_n|\Theta,K) p(y_n|z_n,\Theta,K) = \prod_{n=1}^N \prod_{k=1}^K w_k^{z_{nk}} Normal(y_n;\mu_k,\tau_k^{-1})^{z_{nk}} }[/math]
- Priors: we choose conjuguate ones
- for the parameters: [math]\displaystyle{ \forall k \; \mu_k | \tau_k \sim Normal(\mu_0,(\tau_0 \tau_k)^{-1}) }[/math] and [math]\displaystyle{ \forall k \; \tau_k \sim Gamma(\alpha,\beta) }[/math]
- for the latent variables: [math]\displaystyle{ \forall n \; z_n \sim Multinomial_K(1,\mathbf{w}) }[/math] and [math]\displaystyle{ \mathbf{w} \sim Dirichlet(\gamma) }[/math]
|
Project name
