Sentence examples for maximizing a likelihood from inspiring English sources

For this latter, reconstructions are obtained by maximizing a likelihood function characterizing the physical nature of the phenomenon, a Poisson distribution.

In the case of KHG, which was estimated by maximizing a likelihood of a set of phylogenetic trees of coding sequences of 7332 nuclear protein families taken from Pandit database [38], the total numbers of residues and sites are not written in Kosiol et al. [14], so that an AIC value is not given for KHG in the following.

When they can be identified, they are estimated by optimizing a measure of adequacy between the observed and the model-predicted variance-covariance matrix (e.g. maximizing a likelihood).

A third example is the work of [ 46], aiming to measure the importance of genes by maximizing a likelihood function and identifying those genes that are the most highly connected.

The spatial scan statistic (1) detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape.

Given sequences sampled from multiple time points in the history of a population, we infer selection at each locus by maximizing a likelihood function derived from a multilocus evolution model.

The generated parameter sequence maximizes a likelihood based on not only an HMM likelihood but also a global variance likelihood, resulting in an increase of the global variance of the generated parameter trajectories.

Choosing the pairing of sounds that maximized a likelihood function based on the following model aligned sounds in cognate words from the same etymological class.

The method maximizes a likelihood derived from a statistical model of the relationship between the contact counts and physical distances, and includes an automatic tuning of the parameters defining the link between a 3D distance and the Poisson parameter of the corresponding contact count.

A Bayesian metamodel was developed, and model parameters were calibrated by maximizing a Gaussian likelihood function.

We explore the trade-off maximizing a generative likelihood of labeled and unlabeled data.