Package: MCMChybridGP
Version: 4.2
Title: Hybrid Markov chain Monte Carlo using Gaussian Processes
Author: Mark J. Fielding <mfieldin@uow.edu.au>
Maintainer: Mark J. Fielding <mfieldin@uow.edu.au>
Depends: MASS
Description: 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,
        Technometrics (2010). 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).
License: GPL-2
LazyLoad: yes
Packaged: 2011-07-19 02:24:52 UTC; mark
Repository: CRAN
Date/Publication: 2011-07-23 13:02:16
