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Kaplan and Chen (2014) approximated Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA, but which ignored uncertainty in the propensity score itself.
The fully Bayesian model averaging approach also provided posterior probability intervals of the balance indices.
We utilized an AICc model selection and model averaging approach to test for potential environmental influence on these traits.
Therefore, Kaplan and Chen (2014) provided a fully Bayesian model averaging approach via MCMC to account for uncertainty in both parameters and models.
This model averaging approach is more simple to compute requiring only to fit a simple multiple linear regression model, unlike the VWA approach in which the weighting is estimated for each soil property, and thus in the interests of parsimony is recommended as the model averaging technique to be adopted as protocol.
A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method.
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Covariate balance checks for the case study showed that both Bayesian model averaging approaches offered good balance.
Both Bayesian model averaging approaches offered slightly better prediction of the propensity score compared to the Bayesian approach with a single propensity score equation.
One is model averaging approaches which make some allowance for the uncertainty in choosing an appropriate statistical model (Conigliani and Tancredi, 2009).
A model selection and averaging approach based on information theory was employed to develop a predictive model of conservation planning quality of Florida local governments.
In the present study, the deformation under an active loading of a short misaligned fiber composite is modeled by the orientation averaging approach, employing an analytical description of the behavior of a unit cell (UC), the parameters of which are determined using a FEM analysis of UC response under selected loading modes.
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