Sentence examples for integrated likelihood from inspiring English sources

After integrating over the empirical priors, the dispersion in the integrated likelihood is constant across conditions and different between the genes.

We determine the mixed MAP ML estimator for a model with spurious variants and outliers as well as estimators based on the integrated likelihood.

The following lemma, that was proved by Salazar et al. (2012), provides the tail behavior for the integrated likelihood for p. Provided that n>k+1−a, the integrated likelihood for p under the class of priors (4) is a continuous function in [ 1,∞) and is such that L I (p y)=O(1) as p→∞.

If the integrated likelihood is constant over all models, the PMP is proportional to the marginal likelihood of a specific candidate model, i.e. the probability of the data given that model times a prior probability.

Consider a prior of the form (4). Then the integrated likelihood for p is given by L I ( p ; y ) ∝ ∫ ℝ k ∫ 0 ∞ L ( β, σ, p ; y ) σ − a dσdβ.

The term (p(D|M_k)) can be expressed as an integrated likelihood begin{aligned} p(D|M_k) = int p(D|theta _k,M_k p(theta _k|M_k dtheta _k, end{aligned} (20 where (p(theta _k|M_k)) is the prior density of (theta _k) under model (M_k) Raftery et al.

The relative support for one tree over another, however, is determined by the ratio of the integrated likelihoods for the two topologies, modified by the priors.

For every pair of models, a Bayes factor can be computed, defined as the ratio of their integrated likelihoods.

It could be argued that it would be more sensible to weight models according to their model probabilities by determining the integrated likelihoods of the data that is already available.

For this reason, Haley-Knott type regressions for simple designs [ 14] and variance component methods for more complex designs [ 15] are well adapted, computationally simpler and almost as good [ 16, 17] as full integrated likelihoods [ 18, 19].

Classical statistical criteria were considered to select the models: the log-likelihood and the penalized log-likelihood; namely, the Bayesian information criteria (BIC), the Akaike information criterion (AIC), and the integrated classification likelihood (ICL) [ 25].