 EM for Gaussian mixtures with a common variance

The E-step computes mixture responsibilities ${\tau }_{ij}$ from the old parameters. Put $N_j={\sum }_i{\tau }_{ij}$. The M-step updates ${\pi }_j=N_j/n$, ${\mu }_j={\sum }_i{\tau }_{ij}{y}_i/N_j$ and ${\sigma }^2={\sum }_{i,j}{\tau }_{ij}(y_i-{\mu }_j^{\mathrm{new}}{)}^2/n$. The variance uses new means and old responsibilities. EM likelihood monotonicity guarantees nondecrease of the observed likelihood, not a global optimum.