The Bayesian methods based on posterior probabilities are consistently more accurate than the maximum likelihood baseline.
The proposed Bayesian method based on posterior probabilities is clearly more accurate than the maximum likelihood baseline and also more accurate than the regression method in all cases.
Class assignment was based on posterior probabilities.
The subset of best models used in the model averaging, out of all possible models, was chosen using a type of selection rule called Occam's window (Noble et al. 2009), based on the posterior likelihood for the model.
baySeq [ 6] is based on estimating posterior likelihoods of differential expression via empirical Bayesian methods, assuming negative binomially distributed data.
The functional divergence between specific clades was analyzed using DIVERGE 2.0 [ 36], which can predict the functional divergence of a protein family based on maximum likelihood and posterior probabilities.
This probability of field token value in presence of all values can also be calculated as a posterior probability value based on prior, likelihood of the value given the news reports about the event and the prior probability of occurrence of event in the reports as described in the context of combination and calibration of methods for the purpose of forecasting of events appearing in [133].
Based on genotype likelihood values, we estimated the posterior probability of the minor allele frequency (p i, i = 1,2,…, 2 k) in the sample of 2 k chromosomes, where k is the sample size of breeds [ 8].
And since the importance density should be chosen to represent a close approximation to the posterior, using a better approximation based on the likelihood, rather than the prior, has been shown to improve performance [13].
The final result of the calculation is then a posterior distribution of cumulative incidences based on a likelihood function given by the sequence of positivity observed in the samples and a prior distribution for the cumulative incidences.
