Sentence examples for binary probit regression models from inspiring English sources

Bayesian fitting of binary probit regression models to the training data permits an assessment of the relevance of the metagene signatures in sample classification.

Bayesian fitting of binary probit regression models to the training data then permits an assessment of the relevance of the metagene signatures in within-sample classification, and estimation and uncertainty assessments for the binary regression weights mapping metagenes to probabilities of relative pathway status.

Bayesian fitting of binary probit regression models to the training data then permits an assessment of the relevance of the metagene signatures in within-sample classification, and estimation and uncertainty assessments for the binary regression weights mapping metagenes to probabilities of radiation exposure.

Bayesian fitting of binary probit regression models to the training data permits an assessment of the relevance of the gene-expression signatures in within-sample classification and provides an estimation and uncertainty.

Bayesian fitting of binary probit regression models to the training data then permits an assessment of the relevance of the metagene signatures in within-sample classification, and estimation and uncertainty assessments for the binary regression weights mapping metagenes to probabilities.

To test our hypotheses, a Binary Probit regression model, nonparametric tests and artificial neural networks with BT were employed.

The binary probit regression model revealed that age of the household head, household size, on- farm income and herd size significantly influenced the decision to undertake camel production in the region.

Given a training set of expression vectors (of values across metagenes) representing two biological states, a binary probit regression model is estimated using Bayesian methods.

Given a training set of expression vectors representing two biologic states, a binary probit regression model is estimated by using bayesian methods.

BinReg uses a Bayesian statistical analysis to fit a binary probit regression model on training data given a set of genes that are most correlated with the binary response/phenotype of interest (e.g. Epi-A vs. Non-Epi-A).

Given a training set of metagene scores from samples representing two biological states (for example, pathway-activated and quiescent control), a binary probit regression model was estimated using Bayesian methods.