I have some univariate data which might be well modeled as a two component mixture where the first component is normal with unknown mean and variance and the second is some unspecified continuous ...
So, getting an "idea" of the optimal number of clusters in k-means is well documented. I found an article on doing this in gaussian mixtures, but not sure I am convinced by it, don't understand it ...
I understand the EM algorithm, I understand for example how we get $Q(\theta, \theta^t)$, but I have trouble translating a real-world problem into the EM framework. For example, I'm given this ...