KrigInv-package: Kriging-based inversion of deterministic and stochastic computer codes
Description
Sequential algorithms based on Kriging for computer experiments, meant to explore the subset of input parameters corresponding to a prescribed level of the output.Details
ll{
Package: KrigInv
Type: Package
Version: 1.1
Date: 2010-12-27
License: GPL version 3
LazyLoad: yes
}References
Bect J., Ginsbourger D., Li L., Picheny V., Vazquez E. (2010), Sequential design of computer experiments for the estimation of a probability of failure, accepted with minor revisions to the Journal of Statistics and Computing, http://arxiv.org/abs/1009.5177
Picheny, V., Ginsbourger, D., Roustant, O., Haftka, R.T., Adaptive designs of experiments for accurate approximation of a target region, J. Mech. Des. - July 2010 - Volume 132, Issue 7, http://dx.doi.org/10.1115/1.4001873
Vazquez, E., Bect, J.: A sequential Bayesian algorithm to estimate a probability of failure. In: Proceedings of the 15th IFAC Symposium on System Identification, (SYSID 2009), Saint-Malo, France (2009)
Bichon, B.J., Eldred, M.S., Swiler, L.P., Mahadevan, S., McFarland, J.M.: Efficient global reliability analysis for nonlinear implicit performance functions. AIAA Journal 46 (10), 2459-2468 (2008)
Ranjan, P., Bingham, D., Michailidis, G.: Sequential experiment design for contour estimation from complex computer codes. Technometrics 50(4), 527-541 (2008)
Rasmussen C.E., Williams C.K.I. (2006), Gaussian Processes for Machine Learning, the MIT Press, www.GaussianProcess.org/gpml