Package: MCMChybridGP
Version: 5.4
Title: Hybrid Markov Chain Monte Carlo using Gaussian Processes
Author: Mark J. Fielding <mark.fielding@gmx.com>
Maintainer: Mark J. Fielding <mark.fielding@gmx.com>
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 computationally expensive posterior distributions",
        Fielding et al. (2011) <doi:10.1198/TECH.2010.09195>.
        The research presented in this work was carried out as part of
        the Singapore-Delft Water Alliance Multi-Objective
        Multi-Reservoir Management research programme (R-264-001-272).
License: GPL-2
LazyLoad: yes
Packaged: 2020-11-09 20:50:35 UTC; mark
Repository: CRAN
Date/Publication: 2020-11-12 18:50:02 UTC
NeedsCompilation: yes
Built: R 4.0.2; x86_64-apple-darwin17.0; 2020-11-13 11:26:44 UTC; unix
Archs: MCMChybridGP.so.dSYM
