Sentence examples for binomial likelihood distribution from inspiring English sources

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.

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).

JJ is responsible for the development of the concept and methods, analysis of the example and writing of the manuscript According to the Kaplan-Meier curve, the proportion of people alive at time point t S t that die between time point t and time point t + 1 is equal to (S t - S t +1)/ S t and can be described by binomial likelihood distribution: r t ~ bin(p t, n t).

Since beta prior is conjugate to the binomial likelihood, the posterior distribution will also be a beta distribution: (3) p i j | y i j, n i j, α, β ∼ Beta (α i j *, β i j * ), where α i j * = α + y i j, β i j * = β + n i j − y i j.

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).

Then the importance weight for our desired distribution is the binomial likelihood function: Because Wright-Fisher drift is a Markov process, the importance weight depends only on the allele frequency in the final generation.

Given the prior, the method combines the data of a particular position with Binomial likelihood to produce a posterior allele frequency distribution.

The Beta distribution is conjugate to this binomial likelihood, and is dependent on two parameters, a and b.

This empirical observation is consistent with the classically established prescription for choosing the beta distribution as a conjugate prior to a binomial likelihood.

27 29 31 We evaluated convergence according to Brooks-Gelman-Rubin. 32 The information was imputed according to the arm based approach, and modelled by use of binomial data (binomial likelihood, logit link) or sample means (normal likelihood, identity link) with normal distribution, according to the specific type of outcome explored.

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.