EM algorithm

资 源 简 介

EM algorithm is to solute the problem of parameter maximum likelihood estimation by Dempster， Laind， Rubin in 1977. The EM algorithm can estimate maximum likelihood only through incomplete data set.

详 情 说 明

The EM algorithm was first introduced by Dempster, Laind, and Rubin in 1977. This algorithm was designed to solve the problem of parameter maximum likelihood estimation when dealing with incomplete data sets. Essentially, the EM algorithm is a two-step iterative process that alternates between the expectation step (E-step) and the maximization step (M-step).

During the E-step, the algorithm estimates the expected value of the missing data by using the current estimate of the parameters. In the M-step, the algorithm updates the parameter estimate by maximizing the log-likelihood function with the expected values of the missing data from the previous step. This iterative process continues until the algorithm converges to a set of parameter estimates that maximize the likelihood function.

The beauty of the EM algorithm is that it can handle missing data without having to throw away the entire data set. This is especially useful when dealing with real-world data sets, which often have missing values due to various reasons such as data entry errors or data loss. By using the EM algorithm, researchers and data scientists can make the most out of their data and obtain more accurate parameter estimates.