Sentence examples for optimum likelihood from inspiring English sources

We propose an optimum multibit decision fusion rule derived from the previously known optimum likelihood ratio for a single-hop network with star topology.

The collection of trees produced at this point was pruned heuristically by viewing the output of likelihood scores in MrBayes, and only trees near the optimum likelihood score were retained using the appropriate burn-in criterion.

Summary: We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures.

The THEMATICS method was then combined with geometry features derived from protein structure to predict catalytic residues from enzyme structure using a monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL) [ 13].

To test the performance of SEQ, we compared our prediction results with those of Partial Order Optimum Likelihood (POOL) [ 6], which combines residue electrostatic properties and structure geometry information to predict catalytic residues.

Recently (Tong et al., 2009), we have reported a new machine learning method, partial order optimum likelihood (POOL), which uses input features from THEMATICS and outperforms many of the best prior methods.

When it comes to the DF, the optimum maximum likelihood (ML) detector was proposed in [4, 5].

It is seen that the proposed detectors achieve similar performance to that of well-known optimum Maximum Likelihood Detector (MLD) at a significantly lower computational complexity and outperform the traditional MMSE MUD.

For each COG, the model that is the best fit to its phyletic vector can be found by calculating the Akaike Information Criterion (AIC) [ 27], which is AIC = − 2 l + 2 p, where l is the optimum log likelihood under the model, and p is the number of parameters.

To compute the optimum log-likelihood value for each possible pair of leaf and question, the maximum likelihood estimate of mean parameters has to be first obtained by Equation 18.

This optimum log-likelihood measure is expressed by (22).