Expectation–maximization algorithm - Wikipedia

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.[1] The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated