Sentence examples for binomial likelihood for the from inspiring English sources

We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data.

For example, in Mx one could add an extra data group and specify the binomial likelihood for the prevalence parameter given a data set on n1 affected individuals and n2 healthy individuals.

Additionally, we obtained Bayesian posterior estimates of the probability that FST outlier loci had estimates of α > 0 by specifying a binomial likelihood for the number of FST outlier loci with α > 0 and an uninformative beta prior on pLA (i.e., pLA ~ beta[1, 1]).

Specifying a vague a priori beta distribution on the commuting probability from municipality i to j (p ij), and a binomial likelihood for c ij with binomial denominator equal to the population of i at the 2001 census (pop i), the resulting posterior distribution for p ij was a beta distribution with parameters 1 + c ij and 1 + pop i – c ij.

In the Bayesian hierarchical framework that we consider, the binomial likelihood of the data is the first level of the model, that is, for modeling the within-area variability of the counts conditional on unknown risk parameters.

Assuming a binomial likelihood of the variant depth given a particular allele frequency, total depth, and a prior distribution on those frequencies, we obtained a posterior distribution for the allele frequency for every tentative allele using Bayes theorem.

This test involved calculating the binomial likelihood of the observed frequency distribution of the SNP alleles under the null hypothesis of heterozygosity (i.e., assuming a probability of 0.5 for both alleles).

Fourth, we used a normal approximation to the likelihood, instead of the exact binomial likelihood, in the modeling for meta-analysis.

A binomial likelihood distribution of the incident events for every interval can be described according to: (10) r jkt ~ b i n p jkt, n jkt Where r jkt is the observed number of events in the mth interval ending at time point t m +1 for treatment k in study j.

For both scenarios parameters were estimated by maximising the product of the binomial likelihood across the time series of observed data using "optim" in R [ 25].

A binomial likelihood distribution was used for the incident number of deaths for every two month interval, which was calculated based on the survival percentages from the Kaplan-Meier curves and the number of patients at risk at the beginning of the interval in each arm of each study, assuming a constant hazard rate within each interval (see Jansen and Cope [ 16] for more details).