A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Export

@misc{citeulike:361175, abstract = {We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (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...}, author = {Bilmes, J.}, citeulike-article-id = {361175}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.613}, keywords = {clustering, learning, stats}, posted-at = {2007-12-03 15:10:03}, priority = {2}, title = {A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.613}, year = {1997} }

TY - GEN ID - citeulike:361175 L3 - citeulike-article-id:361175 N2 - We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (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... TI - A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models KW - clustering KW - learning KW - stats AU - Bilmes, J PY - 1997/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.613 ER -