Why is centering and standardizing generally recommended in the Bayesian approach (MCMCregress) ?
Does it apply equally for non-informative as well as informative priors?
EDIT: We have been working on a specific example. The following screen shots provide the summary from the R output. Does the centering and standardizing improve the prediction? How will the parameters be interpreted once they have been centered and standardized?
mcmc.logmod <- MCMCregress(log(Salary) ~ AtBat + Hits + HmRun, data = red.hitters, family = gaussian, burnin = 1000, mcmc = 10000, verbose = 0) summary(mcmc.logmod) plot(mcmc.logmod, col = "brown")
Centered and standardized:
mcmc.logmod2 <- MCMCregress(salary ~ atbat + hits + hmrun, data = sub.hitters, family = gaussian, burnin = 1000, mcmc = 10000, verbose = 0) summary(mcmc.logmod2)