Bayesian analyses study designs

Model centred bayesian analysis (MCBA) builds the inner model space only from the components that can be interpreted as due mainly to nonrandom underlying factors, determined by experimental design, or that show an almost rectangular distribution. In a study on Portuguese olive oils collected in the years 1975-1980 it was seen that the distribution on two eigenvectors (studied by the two-dimensional... 

Bayesian methods from study design through data analysis to decision support... 

In metabolism and nutrition, where each experiment has been designed to be complete and population analysis is used to fill in missing values and to incorporate relative uncertainties into the estimation, a good procedure would be to first examine in detail those individual studies which are the most complete. This will familiarize the user with the behavior of the model, produce initial estimates for the system parameters, provide a chance to verify that these values are reasonable, and allow the use of tools for the identifiability of individual experiments (Jacquez and Perry, 1990). After this exercise has been complete, all experiments, including those that are incomplete, can be pooled for population analysis and testing the effects of covariates. If required, the final step would be to use the estimated distributions to obtain Bayesian parameter estimates for the individual experiments. This procedure should yield the most appropriate estimates for the incomplete experiments. 

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