Package: MCMChybridGP
Version: 2.2
Title: Hybrid Markov chain Monte Carlo using Gaussian Processes
Author: Mark J. Fielding <stafmj@nus.edu.sg>
Maintainer: Mark J. Fielding <stafmj@nus.edu.sg>
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
(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).
License: GPL-2
LazyLoad: yes
Packaged: 2009-12-19 02:43:35 UTC; mark
Repository: CRAN
Date/Publication: 2009-12-20 09:34:38

Package: MCMChybridGP
Version: 3.1
Title: Hybrid Markov chain Monte Carlo using Gaussian Processes
Author: Mark J. Fielding <stafmj@nus.edu.sg>
Maintainer: Mark J. Fielding <stafmj@nus.edu.sg>
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: 2010-09-30 07:18:50 UTC; markjamesfielding
Repository: CRAN
Date/Publication: 2010-09-30 07:41:36