Dan Piponi has written up a simple to follow derivation of the Expectation-Maximization algorithm. It give a very practical derivation of the algorithm which also makes it easy to remember.

What it clarifies for me is the step in the EM algorithm where one introduces auxilliary variables – one for each value hidden value that the hidden variable can take on – which somehow turns out to be the conditional probability of given everything else. Why this turns out to be the case has always been a little fuzzy to me. And Dan’s post clarifies it greatly. The step that determines the auxilliary variables comes from equating the derivative of the log-likelihood and the derivative of the simpler function involving ’s and solving for . Please have a read.

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