Theory and Use of the EM Algorithm introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. It describes in detail two of the most popular applications of EM: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). It also covers the use of EM for learning an optimal mixture of fixed models, for estimating the parameters of a compound Dirichlet distribution, and for disentangling superimposed signals. It discusses problems that arise in practice with EM, and variants of the algorithm that help deal with these challenges.Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use.
Theory and Use of the EM Algorithm introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. It describes in detail two of the most popular applications of EM: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). It also covers the use of EM for learning an optimal mixture of fixed models, for estimating the parameters of a compound Dirichlet distribution, and for disentangling superimposed signals. It discusses problems that arise in practice with EM, and variants of the algorithm that help deal with these challenges.Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use.
The only single-source??now completely updated and revised??to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture...
Approach your problems from the right end It isn't that they can't see the solution. It is and begin with the answers. Then one day, that they can't see the problem. perhaps you will tind the tinal...
This volume contains the papers presented at the 14th Annual Conference on Algorithmic Learning Theory (ALT 2003), which was held in Sapporo (Japan) duringOctober17-19,2003...
This book constitutes the conference proceedings of the 8th International Conference on Algorithmic Decision Theory, ADT 2024, held in New Brunswick, NJ, USA, during October 14-16, 2024.The 18 full...
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