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 |