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Here we describe the data augmentation procedure.
For all tri-categorical items the cut point between the first and the second category is c 1=1. Albert and Chib (1993) propose a data augmentation procedure to sample latent underlying variables in a threshold model.
An expectation-maximization algorithm and the data augmentation procedure integrated in the NORM software were applied.
We inferred the unobserved mutations via a data augmentation procedure that exploits uniformization and is robust to model misspecification (Minin and Suchard, 2008; O'Brien et al., 2009).
We employed several strategies, including a data augmentation procedure which simulates missing species to generate "complete" data sets, to examine the diversification patterns in sigmodontine rodents.
NORM imputes data through the expectation-maximization algorithm, which provides an efficient estimation of means, variances, and covariances, and uses a data augmentation procedure that generates multiple imputations of missing values.
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Predictive distributions (11) were also applied in the data augmentation procedures to handle missing outcome values.
NORM imputes data via an expectation-maximization algorithm, which provides efficient estimation of mean, variances, and covariances and uses a data-augmentation procedure that generates multiple imputations of missing values.
NORM uses a data augmentation (DA) procedure to impute multiple sets of data.
For implementing the Gibbs sampling procedure to estimate parameters and to predict state variables, we use data augmentation method to expand the model.
No radiographic examination immediately after sinus augmentation procedure was undertaken.
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