Sentence examples for maximised likelihood from inspiring English sources

The test statistic for a given gene is generated by comparing the maximised likelihood score for that gene with topology unconstrained, with the likelihood obtained when topology was fixed at the maximum likelihood topology obtained from the concatenated dataset.

Model selection used Akaike's information criterion (AIC), which measures the goodness of fit of a model based on the number of parameters included and the maximised likelihood for the model.

Where greater than one cluster was present (i.e. parameter sets with similar maximised likelihoods) both sets are presented.

In this sampling regime the estimation of T s was strongly dependent on whether infection was detected on day 28 (2nd sampling point) or not, it was this observation process that resulted in different maximised likelihoods from different stochastic observations.

For either model, the likelihood contribution of the ith individual is given by We thus construct likelihood ratio tests of association of an accumulation of rare variants with disease by comparing the maximised likelihoods of two models via analysis of deviance: (i) the null model where λ = 0; and (ii) the alternative model for which λ is unconstrained.

In some instances when intermediate sampling was conducted, only once during the 8 week cohort at week 4, different stochastic observations gave two different estimates of T s with comparable maximised likelihoods, highlighting the presence of two different optima across the 100 stochastic observations.

Effect sizes as estimated from the PGLS model that adjusted for the phylogenetic relationships of species and used statistical weights (log10-sample size) in a combination that offered the best fitted to the data based on maximised log-likelihood.

Generally speaking, for simplicity, the log marginal likelihood is maximised [13]: begin{array}{*{20}l} log pleft(boldsymbol{Y}|boldsymbol{X}, thetaright) =& - frac{1}{2} boldsymbol{Y}^{T} (K + {sigma_{n}^{2}}boldsymbol{I})^{-1}boldsymbol{Y}& - frac{1}{2} log|K + {sigma_{n}^{2}}boldsymbol{I}| - frac{n}{2} log2pi.

We can thus construct a likelihood ratio test by comparing the maximised weighted likelihoods of two models via analysis of deviance: (i) the null model where β = 0; and (ii) the alternative model for which β is unconstrained.

The likelihood ratio test, which is the difference between the maximised log-likelihood statistics, was used to assess the significance of additional covariates in the model (Breslow and Day, 1980).

How can these be maximised?