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).
|Author||Mark J. Fielding <firstname.lastname@example.org>|
|Maintainer||Mark J. Fielding <email@example.com>|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.