Research Article
Year 2014,
Volume: 43 Issue: 5, 885 - 890, 01.10.2014
Abstract
The EM algorithm has become a popular efficient iterative procedure
to compute the maximum likelihood (ML) estimate in the presence
of incomplete data. Each iteration of the EM algorithm involves two
steps called expectation step (E-step) and maximization step (M-step).
Complexity of statistical model usually makes the iteration of maximization step difficult. An identity on which the derivation of the EM
algorithm is based is presented. It is showed that deriving iteration
formula of parameter of hidden binomial trials based on the identity is
much simpler than that in common M-step.
to compute the maximum likelihood (ML) estimate in the presence
of incomplete data. Each iteration of the EM algorithm involves two
steps called expectation step (E-step) and maximization step (M-step).
Complexity of statistical model usually makes the iteration of maximization step difficult. An identity on which the derivation of the EM
algorithm is based is presented. It is showed that deriving iteration
formula of parameter of hidden binomial trials based on the identity is
much simpler than that in common M-step.
References
- . . .
There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Statistics |
| Authors | |
| Publication Date | October 1, 2014 |
| Published in Issue | Year 2014 Volume: 43 Issue: 5 |