An iterative Expectation-Maximization approach allows to compute, for each earthquake i, the background probability as ωi = μ ti /λ ti).
The iterative expectation-maximization (EM) algorithm enables approximating the ML estimate.
The iterative Expectation-Maximization algorithm (EM-algorithm) is used to find the parameters of RBF network (W and β) maximizing the, so called, log likelihood function which measures a correspondence between the data distribution and the model.
When estimating channel parameters in linearly modulated communication systems, the iterative expectation-maximization (EM) algorithm can be used to exploit the signal energy associated with the unknown data symbols.
To accommodate these problems, the iterative expectation-maximization (EM) algorithm [1, 2] can be used to also exploit the signal energy associated with the unknown data symbols during the channel estimation; this way, much less pilot symbols are needed to achieve a given estimation accuracy.
MEME looks for conserved ungapped blocks in a group of sequences using an iterative expectation-maximization algorithm [ 57].
Haplotype frequencies will be estimated using the iterative Expectation-Maximization algorithm [ 110, 111], and their association with each skin cancer type and phenotypic characteristics will be evaluated.
The flexibility in the shape and orientation of clusters can be incorporated using Gaussian mixture models and a Bayesian approach to estimate the support for particular arrangements of clusters using iterative Expectation-Maximization methods for maximum-likelihood [ 28].
