Technical Reports

1998

@techreport{Bil1998,
	vgclass =	{report},
	author =	{Jeff A. Bilmes},
	title =	{A Gentle Tutorial of the {EM} Algorithm and its
	Application to Parameter Estimation for {G}aussian Mixture and Hidden
	{M}arkov Models},
	number =	{TR-97-021},
	institution =	{Computer Science Division, Department of Electrical Engineering and Computer Science, U.C. Berkeley},
	month =	{April},
	year =	{1998},
	url =	{http://crow.ee.washington.edu/people/bulyko/papers/em.pdf},
	abstract =	{We describe the maximum-likelihood parameter estimation
	problem and how the Expectation-Maximization (EM) algorithm can be used
	for its solution. We first describe the abstract form of the EM
	algorithm as it is often given in the literature. We then develop the
	EM parameter estimation procedure for two applications: 1) finding the
	parameters of a mixture of Gaussian densities, and 2) finding the
	parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch
	algorithm) for both discrete and Gaussian mixture observation models.
	We derive the update equations in fairly explicit detail but we do not
	prove any convergence properties. We try to emphasize intuition rather
	than mathematical rigor.},
}