Particle Learning of Gaussian Processes

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Documentation for package ‘plgp’ version 1.1-12

• DESCRIPTION file.
• Code demos. Use demo() to run them.

Help Pages

plgp-package	Particle Learning of Gaussian Processes
addpall.CGP	Add data to pall
addpall.ConstGP	Add data to pall
addpall.GP	Add data to pall
data.CGP	Supply GP data to PL
data.CGP.adapt	Supply GP data to PL
data.ConstGP	Supply GP data to PL
data.ConstGP.improv	Supply GP data to PL
data.GP	Supply GP data to PL
data.GP.improv	Supply GP data to PL
draw.CGP	Metropolis-Hastings draw for GP parameters
draw.ConstGP	Metropolis-Hastings draw for GP parameters
draw.GP	Metropolis-Hastings draw for GP parameters
exp2d.C	2-d Exponential Hessian Data
init.CGP	Initialize particles for GPs
init.ConstGP	Initialize particles for GPs
init.GP	Initialize particles for GPs
lpredprob.CGP	Log-Predictive Probability Calculation for GPs
lpredprob.ConstGP	Log-Predictive Probability Calculation for GPs
lpredprob.GP	Log-Predictive Probability Calculation for GPs
papply	Extending apply to particles
params.CGP	Extract parameters from GP particles
params.ConstGP	Extract parameters from GP particles
params.GP	Extract parameters from GP particles
PL	Particle Learning Skeleton Method
PL.env	Particle Learning Skeleton Method
plgp	Particle Learning Skeleton Method
pred.CGP	Prediction for GPs
pred.ConstGP	Prediction for GPs
pred.GP	Prediction for GPs
prior.CGP	Generate priors for GP models
prior.ConstGP	Generate priors for GP models
prior.GP	Generate priors for GP models
propagate.CGP	PL propagate rule for GPs
propagate.ConstGP	PL propagate rule for GPs
propagate.GP	PL propagate rule for GPs
rectscale	Un/Scale data in a bounding rectangle
rectunscale	Un/Scale data in a bounding rectangle