# Mixture of Gaussian distribution. In the example of Section 20.7, assume that the M distributions are all Gaussian distributions N(μi, σ2 i ),i = 0, 1,…, M − 1. Find the EM algorithm in a form to obtain an MLE of the model parameters μi , σi as well as πi , the probability that the ith distribution is chosen, i = 0, 1,…, M − 1. Also state the algorithm similar to Algorithm 20.5.

Mixture of Gaussian distribution. In the example of Section 20.7, assume that the M distributions are all Gaussian distributions N(μi, σ2 i ),i = 0, 1,…, M − 1. Find the EM algorithm in a form to obtain an MLE of the model parameters μi , σi as well as πi , the probability that the ith distribution is chosen, i = 0, 1,…, M − 1. Also state the algorithm similar to Algorithm 20.5.