Sentence examples for by optimizing a likelihood from inspiring English sources
The method of obtaining estimates of the unknown parameters of the LMM was by optimizing a likelihood function.
In addition, it estimates isoform and gene expression (fragments per kilobase exon per million mapped fragments, or FPKM) by optimizing a likelihood function containing transcript abundances as parameters.
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).
Maximum-likelihood phylogenetic trees were constructed using the TreeFinder program [ 62] by optimizing a default starting tree constructed using the neighbor-joining method with the Whelan and Goldman WAGG) empirical model of substitutions [ 63].
In most studies, the gain function is designed by optimizing a criterion based on some assumptions of the noise and speech distributions, such as minimum mean square error (MMSE), maximum likelihood (ML), and maximum a posteriori (MAP) criteria.
The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction.
To achieve this, we partition the genome into overlapping, contiguous windows of SNPs, and we optimize a likelihood model over each of the windows.
> Finally, in Table 4 we depict the likelihood scores of the trees computed independently by optimizing the likelihood score on the resulting SPR-modified trees obtained by the mesh-based and standard method.
The likelihood of the data from allele A observed in library i can now be expressed as (1) L (D | i α A, β A ) = ∫ 0 1 x a i (1 − x ) b i f (x, α A, β A ) d x The likelihood of the data observed in all cDNA libraries is a product over terms such as this, and the α and β parameters can be estimated by optimizing the likelihood for the combined data set.
In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed.
