# Why can we consider expectation in Gibbs Sampling

Suppose we are doing Gaussian Mixture (1D). The histogram of posterior distribution is (we choose a new \(z_i\) from this histogram),

\(\newcommand{\balpha}{\boldsymbol{\alpha}} \newcommand{\bz}{\mathbf{z}} \newcommand{\cN}{\mathcal{N}} \newcommand{\bx}{\mathbf{x}} \newcommand{\btheta}{\boldsymbol{\theta}} \begin{align} &\qquad \int p(x_i | z_i=k, \mu_k, \sigma^2) p(\mu_k | \bx^{\backslash i}, \bz^{\backslash i}, \mu_P, \sigma^2_P, \sigma^2) d\mu_k \int p(z_i =k|\btheta) p(\btheta | \bz^{\backslash i},\balpha) d\btheta \\ &= \cN(x_i | \mu_{\rm New}, \sigma^2) \cdot \frac{n_k^{\backslash i} + \alpha_k}{\sum_{k=1}^{K} n_k^{\backslash i} + \alpha_k} \end{align}\)

Each integral shows posterior predictive distribution of \(x_i\) and \(z_i\), respectively. We can consider the expectation of \(\mu_k\) for the first term instead of calculating everything. \(\mu_k\) can take various values, but it follows Normal distribution. Enough amount of data makes the posterior distribution of \(\mu_k\) sharp. The expectation can be a good approximation.

\(\newcommand{\balpha}{\boldsymbol{\alpha}} \newcommand{\bz}{\mathbf{z}} \newcommand{\cN}{\mathcal{N}} \newcommand{\bx}{\mathbf{x}} \newcommand{\btheta}{\boldsymbol{\theta}} \begin{align} &\qquad p(x_i | z_i=k, {\mu_k}, \sigma^2) \int p(\mu_k | \bx^{\backslash i}, \bz^{\backslash i}, \mu_P, \sigma^2_P, \sigma^2) d\mu_k\\ &= p(x_i | z_i=k, \overline{\mu_k}, \sigma^2) \end{align}\)

Code can be found here.