The Expectation Maximization (EM) algorithm
نویسنده
چکیده
In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly including the prior term like in MAP-estimation) given by, L(d,θ) = log[p(d|θ)] + log[p(θ)] (2) = log[ ∫ dy p(d,y|θ)] + log[p(θ)] (3)
منابع مشابه
An Explanation of the Expectation Maximization Algorithm, Report no. LiTH-ISY-R-2915
The expectation maximization (EM) algorithm computes maximum likelihood estimates of unknown parameters in probabilistic models involving latent variables. More pragmatically speaking, the EM algorithm is an iterative method that alternates between computing a conditional expectation and solving a maximization problem, hence the name expectation maximization. We will in this work derive the EM ...
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