The authors stressed that the Expectation Maximisation (EM) algorithm sometimes involves complex numerical integration, especially during step E (the algorithm computes the expectation of the log-likelihood evaluated using the current estimate for the parameters), and that MI has the advantage of being computationally much simpler for situations with incomplete continuous variables.
Lemma 4. For the SO-DCTS algorithm in (28), the expectation of the error vector is given by (32).
EM algorithm attempts to maximize the expectation of the logarithm of the joint likelihood of the model.
In IsoEM, insert lengths are combined with base quality scores, and, if available, strand information to probabilistically allocate reads to transcripts during the expectation step of the algorithm.
In IsoEM, insert lengths are combined with base quality scores, and, if available, read pairing and strand information to probabilistically allocate reads to isoforms during the expectation step of the algorithm.
The expectation of maximization and K-Means algorithms are very suitable for the large datasets.
For the DCTS algorithm in (8), the expectation of disagreement vector is (9).
RECOUNT is an efficient implementation of the Expectation Maximization algorithm of Beißbarth et al. that can process large next-generation sequencer datasets.
We introduce an interval version of the Expectation Maximization (EM) algorithm for the identification of the interval parameters of the system.
In this study, allele and haplotype frequencies were analyzed and estimated by means of the expectation maximization (EM) algorithm, exact tests of the Hardy-Weinberg equilibrium were performed using Arlequin software (ver.2.000) [ 15].
