Z Algorithm Algorithm

In statistics, an expectation – maximization (EM) algorithm is an iterative method to find (local) maximal likelihood or maximum a posteriori (map) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (e) step, which makes a function for the expectation of the log-likelihood evaluated use the current estimate for the parameters, and a maximization (M) step, which calculates parameters maximizing the expected log-likelihood found on the e step. The Dempster – Laird – Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. Wu's proof established the EM method's convergence outside of the exponential family, as claimed by Dempster – Laird – Rubin. The Dempster – Laird –

Z Algorithm source code, pseudocode and analysis