CRAN
MCMChybridGP 4.3
Hybrid Markov chain Monte Carlo using Gaussian Processes
Released Aug 12, 2011 by Mark J. Fielding
Dependencies
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).
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies>
<dependency>
<groupId>org.renjin.cran</groupId>
<artifactId>MCMChybridGP</artifactId>
<version>4.3-b241</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:MCMChybridGP')
Test Results
This package was last tested against Renjin 0.9.2644 on Jun 1, 2018.