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To proceed with the module network inference process, we first imputed the missing values in the data by using the Bayesian principal component analysis (BPCA) method [ 18].
Since clustering and network inference need complete data, we imputed missing values using the Bayesian Principal Component Analysis (BPCA) imputation from the R-package 'pcaMethods' [ 38].
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Probability density functions on up to two principal fibre directions were estimated at each voxel in the brain using the Bayesian estimation of diffusion parameters obtained using sampling techniques toolbox (BEDPOSTX; Behrens et al. 2007) implemented in FSL.
Population history was inferred using the Bayesian skyline plot with six groups of coalescent intervals.
A spatial model was calibrated using the Bayesian method.
This point is illustrated using the Bayesian interpretation of regularization.
It was derived using the Bayesian paradigm (Rubin 1987 1996).
Using the Bayesian method of data modelling, the most optimistic estimate researchers could make was that wild thylacines probably became extinct in the 1950s.
All principal components were included to the k-means clustering algorithm from KDAPC=1 to KDAPC=14, and the best-fitting KDAPC was selected using the Bayesian information criterion.
The model was selected using the Bayesian Information criterion.
Others more sophisticated methods like the Bayesian Principal Component Analysis (BPCA) [ 30] combines a principal component regression, a Bayesian estimation and a variational Bayes (VB) algorithm.
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