Sentence examples for using the conditional log-likelihood from inspiring English sources

A number of transmission models, (5), were fitted to the data using the conditional log-likelihood, (7).

Based on this aim and on the available training data, we use the Conditional Log Likelihood (CLL) as the objective function to be optimized when learning each classifier C i. Classifiers whose structures were learnt by optimizing this objective function were found to perform better than classifiers that used other structures [ 38].

Further, we formulate LL phase differently (see below). 1 Based on a widely-used Bayesian model [ 40, 41], the posterior log-likelihood is proportional to the conditional log-likelihood logP(D i | G i ), which is the log-probability of observing the piled up bases (D i ) given the genotype of the allele pair interested (G i ) at site i.

edgeR maximizes a weighted combination of the conditional log-likelihoods with per-gene dispersion and of the conditional log-likelihood with common dispersion.

The conditional log-likelihood 2010 can then be maximized to yield joint estimates for μ1, μ2, and μ3.

Maximum Likelihood estimation maximizes the conditional log likelihood over the labeled training examples.

This is due to the convex characteristic of the conditional log likelihood function in CRFs.

For this case, we proposed using the conditional maximum likelihood (CML) algorithm to obtain the estimates.

Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited.

Results show that conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring.

Conditional log-likelihood scoring is developed for structural learning on continuous time Bayesian network classifiers.