= Finite Gaussian mixture with a common variance
{title2=$f(y)=\sum_j\pi_j\phi(y;\mu_j,\sigma^2)$}
= Finite Gaussian mixtures with a common variance
{synonym}
A finite Gaussian mixture with a common variance has density $f(y)=\sum_{j=1}^k\pi_j\phi(y;\mu_j,\sigma^2)$, with positive common variance and weights summing to one. The means locate its latent components, which need not correspond to distinct modes. It has $2k$ free parameters: $k-1$ weights, $k$ means and one variance. <EM for Gaussian mixtures with a common variance> supplies iterative likelihood updates.
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