Hybrid Markov chain Monte Carlo (MCMC) to simulate from a multimodal
target distribution. A Gaussian process approximation makes this possible
when derivatives are unknown. The Package serves to minimize the number of
function evaluations in Bayesian calibration of computer models using
parallel tempering. It allows replacement of the true target distribution
in high temperature chains, or complete replacement of the target. Methods
used are described in, "Efficient MCMC schemes for Bayesian calibration of
computer models", Fielding, Mark, Nott, David J. and Liong Shie-Yui
(2009), in preparation. The authors gratefully acknowledge the support &
contributions of the Singapore-Delft Water Alliance (SDWA). The research
presented in this work was carried out as part of the SDWA's
Multi-Objective Multi-Reservoir Management research programme
(R-264-001-272).