pGPx is a R package to generate pseudo-realizations of Gaussian process excursions sets. The paper Azzimonti et al. (2016) and the manuscript Azzimonti (2016) provide explanations for the problem and the methods.


The package provides approximate posterior realizations over large designs by simulating the field at few well chosen points and interpolating. The simulation points are chosen minimizing the (posterior) expected distance in measure between the approximate excursion set and the full excursion set. The main functions in the package are:





Azzimonti, D. and Bect, J. and Chevalier, C. and Ginsbourger, D. (2016). Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior. SIAM/ASA Journal on Uncertainty Quantification, 4(1), 850-874. DOI: 10.1137/141000749. Preprint at arXiv:1501.03659

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

Felzenszwalb, P. F. and Huttenlocher, D. P. (2012). Distance Transforms of Sampled Functions. Theory of Computing, 8(19):415-428.