The EM algorithm is a popular method for maximum likelihood estimation. Its simplicity in many applications and desirable convergence properties make it very attractive. Its sometimes slow convergence ...

This example estimates the normal SSM of the mink-muskrat data using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM ...

We developed an expectation–maximization (EM) algorithm to estimate the variance parameter of the prior distribution for each regression coefficient.

Kenneth Lange, A Gradient Algorithm Locally Equivalent to the EM Algorithm, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 57, No. 2 (1995), pp. 425-437 ...

This is reasonable in many tomographic problems. The step of the MART algorithm for (4.1) is as follows: MART is an example of a multiplicative algorithm, see Pierro (1990); another example is the EM ...