Sentence examples for binomial likelihood of the from inspiring English sources

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).

Assuming a binomial likelihood of the data, the posterior density on (f, e) is P f, e = p f E ⋅ C (f + e - 2 e f ) n (1 - f - e + 2 e f ) m - n where the constant C ensures that ∫ P f, e d f d e = 1.

21 As an alternative to collapsing upper comorbidity categories to depict the association between comorbidity and all-cause mortality, we generated a cubic power function for mortality risk by maximizing the log-binomial likelihood of the observed data, using the entire range of comorbidity counts in the cohort (0 to 7 comorbidities).

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]).

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].

The meta-analysis carried out under the Bayesian framework, on the 18 countries and applying binomial likelihood, allowed the estimation of the uncertainty around the between country heterogeneity (τ = 1.73 (95% credible intervals: 0.98, 3.47).

For example, when analysing count data, contrast the use of a negative binomial likelihood with the Poisson, as was employed in two of the transmission models developed to estimate the evolution of the 2009 A/H1N1 influenza pandemic (Birrell et al., 2011; Dorigatti et al., 2012).

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