For the computation of uncertainty intervals for our model predictions we propose to follow an approach, based in the method described in Section 2, consisting of three stages: clustering, parameter estimation via maximum likelihood, and probability interval computation.
Our mixture method produces more accurate ancestral genomes compared with the maximum likelihood method while the computation time is far less than that of the parsimony-based direct optimization methods.
Experiments show that our mixture method produces more accurate ancestral genomes than the maximum likelihood method while the computation time is far less than that of pure median method.
In the experiments, performance evaluation is carried out by comparing the average localization error (E|mathbf {theta }-hat {mathbf {theta }}|^{2}) computed from the maximum likelihood (ML) estimation technique for fast computation.
A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain.
Subsequently, mixed modeling has become a major area of statistical research, including work on the computation of maximum likelihood estimates, nonlinear mixed effect models, missing data in mixed effects models, and Bayesian estimation of mixed effects models (West et al. 2007).
For ease of computation, the maximum likelihood estimation objective function is usually expressed as the negative sum of the log of the likelihoods, yielding a single number the maximum likelihood estimation objective function value (OFV).
This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function.
Both models were optimized by evolutionary computation, using a maximum likelihood estimator.
It is an iterative computation method of maximum likelihood estimates of missing data from observable variables.
