# User:Timothee Flutre/Notebook/Postdoc/2012/08/16

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## 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, $y_1, \ldots, y_N$, gathered into the vector $\mathbf{y}$.

• 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 (wk), the means (μk) and the precisions (τk) of each mixture components, all gathered into $\Theta = \{w_1,\ldots,w_K,\mu_1,\ldots,\mu_K,\tau_1,\ldots,\tau_K\}$. There are two constraints: $\sum_{k=1}^K w_k = 1$ and $\forall k \; w_k > 0$.

• Observed likelihood: $p(\mathbf{y} | \Theta, K) = \prod_{n=1}^N p(y_n|\Theta,K) = \prod_{n=1}^N \sum_{k=1}^K w_k Normal(y_n;\mu_k,\tau_k^{-1})$

• Latent variables: let's introduce N latent variables, $z_1,\ldots,z_N$, gathered into the vector $\mathbf{z}$. Each zn is a vector of length K with a single 1 indicating the component to which the nth observation belongs, and K-1 zeroes.

• Augmented likelihood: $p(\mathbf{y},\mathbf{z}|\Theta,K) = \prod_{n=1}^N p(y_n,z_n|\Theta,K) = \prod_{n=1}^N p(z_n|\Theta,K) p(y_n|z_n,\Theta,K) = \prod_{n=1}^N \prod_{k=1}^K w_k^{z_{nk}} Normal(y_n;\mu_k,\tau_k^{-1})^{z_{nk}}$

• Priors: we choose conjuguate ones
• for the parameters: $\forall k \; \mu_k | \tau_k \sim Normal(\mu_0,(\tau_0 \tau_k)^{-1})$ and $\forall k \; \tau_k \sim Gamma(\alpha,\beta)$
• for the latent variables: $\forall n \; z_n \sim Multinomial_K(1,\mathbf{w})$ and $\mathbf{w} \sim Dirichlet(\gamma)$