likelihood in EM algorithm -

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regarding em algorithm. if understand correctly, likelihood obtained in e-step (for instance in baum–welch algorithm likelihood can obtained form forward-backward procedure). however, final step in each em-iteration m-step. means likelihood computed in step k, "belongs" parameters of step (k-1). so, or missing something?

the em algorithm aims at, given model, finding parameters of model maximize likelihood of data.

the focus on parameters , not on likelihood value.

from understanding, e-step meant compute expected value of log-likelihood function. in practise, consists in computing values needed m-step, using current parameters values. in m-step, parameters maximize likelihood (or lower bound). can subsequently compute likelihood value new parameters.

are working hmms? if yes, can specify quantities computed @ each step.

hope can help! :)


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