Compute High Dimensional Orthant Probabilities

Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.


anMC is a R package to efficiently compute orthant probabilities of high-dimensional Gaussian vectors. The method is applied to compute conservative estimates of excursion sets of functions under Gaussian random field priors. This is an upgrade on the previously existent package ConservativeEstimates. See the paper Azzimonti, D. and Ginsbourger D. (2018) for more details.

The package main functions are:

  • ProbaMax: the main function for high dimensional othant probabilities. Computes P(max X > t), where X is a Gaussian vector and t is the selected threshold. The function computes the probability with the decomposition explained here. It implements both the GMC and GANMC algorithms. It allows user-defined functions for the core probability estimate (defaults to pmvnorm of the package mvtnorm) and the truncated normal sampler (defaults to trmvrnorm_rej_cpp) required in the method.

  • ProbaMin: analogous of ProbaMax but used to compute P(min X < t), where X is a Gaussian vector and t is the selected threshold. This function computes the probability with the decomposition explained here. It implements both the GMC and GANMC algorithms.

  • conservativeEstimate : the main function for conservative estimates computation. Requires the mean and covariance of the posterior field at a discretization design.

Installation

To install the latest version of the package run the following code from a R console:

if (!require("devtools"))
  install.packages("devtools")
devtools::install_github("dazzimonti/anMC")

References

Azzimonti, D. and Ginsbourger, D. (2018). Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Journal of Computational and Graphical Statistics, 27(2), 255-267. DOI: 10.1080/10618600.2017.1360781. Preprint at hal-01289126

Azzimonti, D. (2016). Contributions to Bayesian set estimation relying on random field priors. PhD thesis, University of Bern. Available at link

News

anMC 0.2.1

Changes with respect to anMC 0.2.0

  • Fixed an issue in trmvrnorm_rej_cpp that could prevent a call to the function conservativeEstimate from inside another package.

  • updated references.

Changes with respect to anMC 0.1.0

  • The package requires a C++11 compiler.

  • The time measurements now are taken with the chrono library from C++11.

  • Dropped dependency on microbenchmark package.

Major changes with respect to ConservativeEstimates 0.2.0

  • The function selectEq is now called selectActiveDims

  • The variable Thresh is now called threshold

  • The functions ProbaMax and ProbaMin now have a user option to choose the function trmvrnorm to generate truncated multivariate normal samples. The default option is trmvrnorm_rej_cpp.

Reference manual

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install.packages("anMC")

0.2.1 by Dario Azzimonti, 7 months ago


Browse source code at https://github.com/cran/anMC


Authors: Dario Azzimonti


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp

Depends on mvtnorm

Suggests DiceKriging, TruncatedNormal, tmg

Linking to Rcpp, RcppArmadillo


Imported by KrigInv.

Suggested by pGPx.


See at CRAN