The EM algorithm guarantees convergence to a locally optimal solution and, depending on the initial values, converges to one of several globally optimal solutions with high probability.

The EM algorithm is a very popular and widely applicable algorithm for the computation of maximum likelihood estimates. Although its implementation is generally simple, the EM algorithm often exhibits ...

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

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 also consider the ECME algorithm, which is not a data augmentation scheme but still aims at accelerating EM. Our numerical experiments illustrate the advantages of the component-wise EM algorithm ...

Recently, TaqMan® assays have been developed for detection of genetic variation at gene level using primers and probes designed for genomic DNA sequences. The R package TaqGCN contains classes and ...

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 ...