Particle Learning of Gaussian Processes

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


Reference manual

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1.1-7 by Robert B. Gramacy, 7 years ago

Browse source code at

Authors: Robert B. Gramacy <[email protected]>

Documentation:   PDF Manual  

Task views: Design of Experiments (DoE) & Analysis of Experimental Data

LGPL license

Depends on mvtnorm, tgp

Suggests ellipse, splancs, akima

Imported by AHM, SPOT, maximin.

Suggested by dynaTree, reglogit.

See at CRAN