Machine Learning, Clustering, Gaussian Mixture Model.
because the likelihood function is independent of q(z), and so maximizing lower bound is equivalent to minimizing the KL divergence of q(z) and p(z|y, θ(t)). Therefore, the E step gives q(z) = p(z|y, θ(t)). On the other hand, the M step tries to maximize the lower bound function with respect to