## Software and algorithms for REA Bayes
(2004)

A companion to:
Tebaldi, Smith
Nychka and Mearns (2004)
*
Quantifying Uncertainty in Projections of Regional Climate Change: A
Bayesian Approach to the Analysis of Multimodel Ensembles
*
The form of full conditional distributions and the steps of the Gibbs
sampler are presented
in the Supplement
PDF 100K

The Markov Chain Monte Carlo was implemented in the R statistical
language. This is a Matlab-like, high level langauage well sutied to
statistics and probability. It is freely available from
Comprehensive R Archive for a variety
of
platforms.
(Our calculations were run on a LINUX PC.)

To reproduce the analysis and to persue the programs transfer the two
files ** REA.data.r ** ** REA.Gibbs.r** from the directory
REA.Bayes

Install the R package and in R

source("REA.data.r")
source("REA.Gibbs.r")

You need to do this only once provided you save your work space when
quitting.
The naming convention for data tables is:

*(scenario).season.variable*

The X and Y variables are matrices where rows index regions and columns
index models. Thus ** A2.DJF.Y ** is a 22X9 matrix of the future
projections for the A2 scenario in winter. Part of the reason that the
data are grouped together is that the Gibbs sampler is more efficient
sampling for all regions simultaneously. (For those familiar with R
programming we have eliminated an explicit loop over regions using
matrix multiplications.)