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.
Maintainer: Dario Azzimonti dario.azzimonti@gmail.com (ORCID) [copyright holder]
Efficient estimation of high dimensional orthant probabilities. 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. It implements the GANMC algorithm and allows for user-defined sampler and core probability estimates.
ProbaMin: analogous of ProbaMax for the problem \(P(min X < t)\), where \(X\) is a Gaussian vector and \(t\) is the selected threshold. It implements the GANMC algorithm and allows for user-defined sampler and core probability estimates.
conservativeEstimate: the main function for conservative estimates computation. Requires the mean and covariance of the posterior field at a discretization design.
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. tools:::Rd_expr_doi("10.1080/10618600.2017.1360781")
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Useful links:
tools:::Rd_expr_doi("10.1080/10618600.2017.1360781")