11 Facts About Expectation-maximization algorithm

In statistics, an expectation–maximization Expectation-maximization algorithm is an iterative method to find maximum likelihood or maximum a posteriori estimates of parameters in statistical models, where the model depends on unobserved latent variables.

EM Expectation-maximization algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin.

EM Expectation-maximization algorithm is used to find maximum likelihood parameters of a statistical model in cases where the equations cannot be solved directly.

EM Expectation-maximization algorithm proceeds from the observation that there is a way to solve these two sets of equations numerically.

EM Expectation-maximization algorithm seeks to find the MLE of the marginal likelihood by iteratively applying these two steps:.

EM Expectation-maximization algorithm can be viewed as two alternating maximization steps, that is, as an example of coordinate descent.

EM Expectation-maximization algorithm is widely used in medical image reconstruction, especially in positron emission tomography, single-photon emission computed tomography, and x-ray computed tomography.

In structural engineering, the Structural Identification using Expectation Maximization Expectation-maximization algorithm is an output-only method for identifying natural vibration properties of a structural system using sensor data .

Parameter-expanded expectation maximization Expectation-maximization algorithm often provides speed up by "us[ing] a `covariance adjustment' to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data".

The a-EM Expectation-maximization algorithm leads to a faster version of the Hidden Markov model estimation Expectation-maximization algorithm a-HMM.

EM Expectation-maximization algorithm has been implemented in the case where an underlying linear regression model exists explaining the variation of some quantity, but where the values actually observed are censored or truncated versions of those represented in the model.