In the sequel, we refer to (7) as the maximum likelihood frequency estimator (MLFE).
Under assumption that the residual frequency offset is lower than the subcarrier spacing, the maximum likelihood frequency offset estimator is given by Δ ^ f ML = 1 2 π Δ t angle ∑ k = 1 N c s 1 [ k ] s 2 * [ k ].
We focus on the receiver signal processing algorithms and derive a maximum likelihood frequency-domain detector that takes into account the presence of impulse noise as well as the intercode interference (ICI) and the multiple-access interference (MAI) that are generated by the frequency-selective power line channel.
We propose a correction, and a maximum likelihood frequency parameterization and show that both these approaches are not similarly biased, and therefore advocate their use in codon models.
SNP Alyze software uses an expectation-maximization algorithm that determines the maximum-likelihood frequencies of multi-locus haplotypes in diploid populations.
The haplotype frequencies were estimated by using an expectation-maximization algorithm to determine the maximum-likelihood frequencies of multi-locus haplotypes [ 48].
In the second approach, we used the EM algorithm to estimate the maximum likelihood haplotype frequencies (see Arlequin manual 3.5) and calculated standard deviations through bootstrap followed by exact tests of LD based on the Markov chain approach (No. of steps in Markov chain = 100,000 and No. of dememorization steps = 1,000).
The sequence haplotypes and their associated maximum likelihood (ML) frequencies were inferred separately for each population sample, using the Bayesian approach based on an approximate coalescent model implemented in the software PHASE v.2.1 [ 93, 94], and the maximum likelihood (ML) approach based on the expectation-maximisation (EM) algorithm implemented in Arlequin ver. 3.5 [ 95].
The NLS method in combination with the criterion (85) yields both a maximum likelihood fundamental frequency estimate and a MAP order estimate (see [27] for details) and it is asymptotically a filtering method as described in Sect.
Using maximum likelihood analysis, average frequencies of the characters were counted across 10,000 trees randomly sampled from the two Metropolis-coupled Markov Chain Monte Carlo (MC) searches of the Bayesian tree reconstruction.
Using the maximum likelihood method, the frequency of cells producing msRNA is calculated.
