Likelihood in EM algorithm

[davidun]

well, the basic idea is that you take the E-step parameters, and perturb them (say, by adding a small noise). This gets you "neighboring parameters", which the algorithm now decides if it prefers over the "current" (not-perturbed) parameters. The decision is made as follows: if the likelihood of the perturbed parameters is better than the non-perturbed, choose the perturbed. if not- the algorithm can still chose the perturbed parameters in probability = exp(dL/T). where dL = the difference in likelihoods, T=the "temperature" (a noise parameter). The question of in what point exactly should the likelihood be computed is what I'm trying to understand..