September 2004
Intermediate to advanced
496 pages
13h 57m
English
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Appendix A. Convergence Properties of EM
The EM algorithm is a general technique for maximum-likelihood estimation (MLE). This appendix provides proof showing that the likelihood function is guaranteed to increase during EM learning. For more convergence properties of the EM algorithm, see [74, 350, 389].
Because sample density p(x(t)|ω) depends on the parameters w, the density function can be rewritten as
For simplicity, the indicator w is omitted:
Following the preceding notation, the log-likelihood to be maximized is rewritten as
Equation ...
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