Questions tagged [expectation-maximization]

While studying machine learning algorithms, I often see the term "expectation-maximisation" (or EM), and how it is used to estimate parameters, where the model depends on unobserved latent variables. The way I see it, it is like a…

EM(Expectation Maximum) Target: maximize $p_\theta(x)$ $ p_\theta(x)=\frac{p_\theta(x, z)}{p_\theta(z \mid x)} \\\\$ Take log on both sides: $ \log p_\theta(x)=\log p_\theta(x, z)-\log p_\theta(z \mid x) \\\\$ Introduce distribution $q_\phi(z)$: $…

The E step on the EM algorithm asks us to set the value of the variational lower bound to be equal to the posterior probability of the latent variable, given the data points and parameters. Clearly we are not taking any expectations here, then why…

I am learning the expectation-maximization algorithm from the article Semi-Supervised Text Classification Using EM. The algorithm is very interesting. However, the algorithm looks like doing a circular inference here. I don't know am I…