Sentence examples for maximum posterior estimate from inspiring English sources

Then, for the fixed maximizing hyper-parameters, the maximized solution of the penalized log-likelihood in (3) is nothing but the optimal maximum posterior estimate, i.e., the mode of the posterior density.

Markov Chain Monte Carlo sampling provided maximum posterior estimates of quantitative T2* and their uncertainty, allowing delineation of the stria of Gennari over the entire length and width of the calcarine sulcus.

Fig. 4. Maximum posterior estimates of respective parameter functions of the hierarchical space-time ETAS model and b-values, applied to the reprocessed JMA data with earthquakes of M 5.0 or larger during the period of 1926 2008; in addition, we use earthquakes of M 6.0 or larger from the precursory period from 1885 1925 as the occurrence history of the ETAS model.

The Kalman filter can be either viewed as a minimum mean square estimates or a maximum posterior estimates.

In cases of significant differences we calculated Bayesian maximum posterior estimates as well as highest posterior density intervals with 95% support (HPDI95%) for the interaction effects.

Hence, if the selected model included interaction effects, the model was again fit with MCMCglmm to obtain Bayesian maximum posterior estimates and highest posterior density intervals with 95% support (HPDI95%) for parameter estimates of interaction effects [93].

The Dirichlet component means were calculated from the Maximum Posterior Estimates (MPE) of the model hyperparameters [ 28].

The maximum posterior probability estimate of the index of stability α was 1.55 and the Brownian motion model was soundly rejected in favour of the stable model under the BPIC model selection criterion (ΔBPIC = 465).

Given measured BOLD responses, maximum a posterior estimates of the parameters in equation (3) can be obtained through an optimization scheme based on variational Bayes (Friston et al. 2003).

In addition to discussing maximum posterior probability estimators, this article reports on a simulation study comparing three approaches to estimating map accuracy: 1) post-classification sampling, 2) resampling the training sample via cross-validation, and 3) maximum posterior probability estimation.

Part of the explanation is likely to rest in using the maximum posterior network as the estimated network.