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 ...

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 ...

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 ...

Berkeley Lab researchers have developed the first 3-D atomic-scale model of P22 virus that identifies the protein interactions crucial for its stability. "This is a great example of how to exploit ...