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Expectation Maximization Algorithm
The EM algorithm is an iterative optimization strategy. Since each iteration in its calculation method is divided into two steps, one is the expectation step (E step) and the other is the maximization step (M step), so the algorithm is called EM Algorithm (Expectation-Maximization Algorithm). The EM algorithm was originally to solve the problem of parameter estimation in the case of missing data.
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Application Scenarios of Using EM Algorithm
Jensen's Inequality
Why is it hard to approximate latent variables?
The relationship between EM algorithm with Jensen's Inequality
The Coin Example of EM Algorithm
The student example of EM algorithm
Convergence of EM Algorithm
Global Optimum of EM Algorithm
A Helpful Presentation Explaining mathematical Details and Applications of EM Algorithm Provided by Berkeley
A Coordinate Ascent View of Understanding EM Algorithm
E Step
M step