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About Lecture 7 PDF
The document discusses the Expectation-Maximization (EM) algorithm and Hidden Markov Models (HMMs). It introduces EM as an algorithm for maximum likelihood estimation with latent variables. The EM algorithm iterates between an expectation (E) step, where the distribution of the latent variables is computed, and a maximization (M) step, where the parameters are re-estimated to maximize the expected log-likelihood. HMMs are presented as a model with latent variables where EM can be applied through the Baum-Welch algorithm. The document provides theoretical details on how EM coordinates ascent of the expected log-likelihood.
Detailed Information
| Author: | ['sdede'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 23 |
| Language: | English |
| Format: | |
| Price: | FREE |
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