plgp (version 1.1-2)

plgp-package: Particle Learning of Gaussian Processes

Description

Sequential Monte Carlo inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL). The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic is provides for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos

Details

For a fuller overview including a complete list of functions, and demos, please use help(package="plgp").

References

Gramacy, R. and Polson, N. (2011). Particle learning of Gaussian process models for sequential design and optimization. Journal of Computational and Graphical Statistics, 20(1), pp. 102-118; arXiv:0909.5262 Gramacy, R. and Lee, H. (2010). Optimization under unknown constraints. Bayesian Statistics 9, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (Eds.); Oxford University Press Carvalho, C., Johannes, M., Lopes, H., and Polson, N. (2008). Particle Learning and Smoothing. Discussion Paper 2008-32, Duke University Dept. of Statistical Science.

http://faculty.chicagobooth.edu/robert.gramacy/plgp.html

See Also

PL, tgp