Sentence examples for maximum likelihood directions from inspiring English sources

After edges are placed, maximum likelihood directions determined, and NC pruning is carried out, the graph is pruned using the data processing inequality method as introduced in ARACNE [ 20].

We investigate the maximum likelihood (ML) direction-of-arrival (DOA) estimation of multiple wideband sources in the presence of unknown nonuniform sensor noise.

In this method a parameter is progressively moved away from its maximum likelihood estimate in either direction (while the other model parameters are optimised) until the difference in fit, distributed as a chi-square with one degree of freedom, is significant.

The attraction of regarding it as a model in its own right is that it becomes possible to derive the asymptotic distribution of the maximum likelihood estimator and generalize in various directions.

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use.

One of the most important ideas on this direction is Huber's M-estimator (maximum likelihood type estimator).

Moreno et al. [ 3] reported that marginal maximum likelihood yielded biased inferences about the variance component, and that the direction of the bias depended on the amount of information associated with either fixed or random effects.

We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions.

An attempt in this direction is made in the form of application of existing maximum likelihood linear regression (MLLR) adaptation which has been primarily used to model the differences due to changes in environment or non nativeness of speakers.

Another interesting direction would be the transfer of profile distances into the maximum likelihood tree estimation procedures.

We used three methods, NJ, Bayesian inference (BI), and maximum likelihood (ML), to construct more refined phylogenetic trees of the detected candidates for the purpose of inferring the directions and possible donors of HGT events.