View source for Expectation-maximization algorithm

This method will be to define a set of citation
profiles and then self-consistently assign each case
to the profile it best fits while at the same time adjusting
the shape of the profiles to best fit the cases assigned to
them. The means by which they accomplish this task is the
expectation-maximization (EM) algorithm.


The EM algorithm is an established tool of statistics,
but one that is relatively new to network analysis. This paper describe a different
application to the analysis of the temporal profiles of
citations.


In essence the EM algorithm is a method for fitting a
model to observed data by likelihood maximization, but
differs from the maximum likelihood methods most often
encountered in the physics literature in that it does not
rely upon Markov chain Monte Carlo sampling of model
parameters. Instead, by judicious use of “hidden” variables,
the maximization is performed analytically, resulting
in a self-consistent solution for the best-fit parameters
that can be evaluated using a relatively simple iteration
scheme.












== Reference ==

[http://en.wikipedia.org/wiki/Expectation-maximization_algorithm Expectation-maximization algorith on Wikipedia].

[http://www.ebook3000.com/The-EM-Algorithm-and-Extensions_4742.html G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions].    (Warning: this is an e version of this excellent book, I put it here just for introduction purpose, if you like this book, please borrow it in a library or buy it)