I am having problems finding a well thought out complete explanation of expectation maximization. Does anyone have a best source for someone completely new to this stuff?
Check out the following tutorials:
T. K. Moon, "The expectation-maximization algorithm", IEEE Signal Processing Magazine, vol. 13, no. 6, pp. 47-60, 1996.
J. A. Bilmes, "A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models".
Of course, you can refer to the original paper by Dempster et al. But it might be slightly hard for a first read.
Another reference is the Pattern Recognition and Machine Learning book by C. Bishop. It has a nice (and intuitive) explanation for EM.