Sentence examples for maximum marginal likelihood from inspiring English sources

The model parameters were estimated using a maximum marginal likelihood approach.

In the Maximum Marginal Likelihood Estimation approach integration over random effects is approximated by numerical integration.

We estimate g from the data by maximum marginal likelihood, where the likelihood is summed over the model space.

Several (mathematical) details of the procedure, such as approximate equivalence to maximum marginal likelihood, convergence criteria and various types of parametric priors are further discussed in [ 3].

This study incorporates existing signaling biology using an informative prior distribution on the network, and its weight of contribution is measured with an empirical Bayes analysis, maximum marginal likelihood.

The analysis was repeated ten times for each maximum K and the log marginal likelihood value for each genetic partition was evaluated.

Bayesian results were comparable to maximum likehood method but they avoided convergence problems, the marginal likelihood allowed to compare all models, and credible interval gave directly the uncertainty of sorption parameters θ.

The marginal likelihood (approximated by the harmonic mean of the maximum likelihoods in BayesTraits when the Markov chain has run for a sufficient number of iterations; see BayesTraits manual) of a model is the integral of the model likelihoods over all values of the model's parameters and over all tree results.

We used the "Trace Character History" analysis with a symmetric, one-parameter Markov k-state model [ 104, 105], which computes likelihoods for categorical characters, and reconstructs ancestral states by the maximum marginal probability (MLE) criterion.

Analytical solutions for the marginal likelihood do not exist; therefore, several methods were implemented approximating the marginal likelihood while searching for the maximum likelihood.

For the estimation of parameters a penalized marginal likelihood approach is proposed which may be based on integration techniques like Gauss–Hermite quadrature but may as well be used within the more recently developed nonparametric maximum likelihood approaches.