Sentence examples for of marginal likelihood from inspiring English sources

The focus is on two methods of marginal likelihood estimation.

In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented.

The hyperparameters, α and β, can be selected through the maximization of marginal likelihood technique proposed by Kuwatani et al. (2014a).

Because of nonlinearity within the evaluation function (Eq. 4), the maximization of marginal likelihood technique for all three hyperparameters is computationally high-cost and non-stable.

The multi-try differential evolution adaptive Metropolis (MT-DREAMzs) algorithm is effective and robust in searching complex probability space, and it is incorporated into NSE to improve the performance of marginal likelihood estimation in this study.

Twice the difference in loge space of marginal likelihood between any two models is the Bayes Factor, 2loge(BF).

In what follows we only review prominent techniques that have led to philosophical debate: Akaike's information criterion, the Bayesian information criterion, and furthermore the computation of marginal likelihoods and posterior model probabilities, both associated with Bayesian model selection.

Because the two models are non-nested, and model comparison and assessment of the approaches in a real data setting is one of our central goals, we formulate the discussion from a Bayesian perspective, comparing the two models in terms of marginal likelihoods and Bayes factors, and in terms of inferences about the treatment effects.

Bayesian inference and the estimation of marginal likelihoods are dependent on the choice of priors.

The model comparison was done using Bayes factors that need the accurate calculation of marginal likelihoods.

To calculate the ratio of marginal likelihoods we need only an estimator P ˆ of P(K =0| Y, ℳRLC).