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 ...

Maximum likelihood estimation in finite mixture distributions is typically approached as an incomplete data problem to allow application of the expectation-maximization (EM) algorithm. In its general ...

The idea is to reduce the cost of calculating the EM algorithm by using a haplotype-grouping preprocess exploiting the symmetrical and inclusive relationships of haplotypes based on the Hardy ...

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 ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

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 ...

The basic reconstruction algorithm is EM (Expectation Maximization) or OSEM (Ordered Subset Expectation Maximization). Variational penalty terms can be included. Extensions allow to estimate motion ...