GP.summary | R Documentation |
Summary of posterior inference on the Bayesian Gaussian process regression model
GP.summary(GP_fit)
GP_fit |
An output object of function GP.Bayes.fit or GP.fast.Bayes.fit. Please refer to them for details. |
A list object consisting of the following elements:
A list object for posterior mean of the target function,consisting of two elements (f is a vector for function values; x is a vector or matrix for points evaluated).
A matrix of real numbers for the standardized grid points for the model fitting. It has the same dimension as "x".
A list object for 95% upper bound of the creditible interval (uci) of the taget function, consisting of two elements (f is a vector for function values; x is a vector or matrix for points evaluated).
A list object for 95% lower bound of the creditibel interval (lci) of the taget function, consisting of two elements (f is a vector for function values; x is a vector or matrix for points evaluated).
A vector of posteror mean, the 95% lcl and ucl for variance of the random error.
A vector of posterior mean, the 95% lcl and ucl for variance of the target function (hyperparameters).
Jian Kang <jiankang@umich.edu>
library(BayesGPfit) library(lattice) set.seed(1224) dat = list() dat$x = GP.generate.grids(d=2,num_grids = 30) curve = GP.simulate.curve.fast(dat$x,a=0.01,b=0.5,poly_degree=20L) dat$f = curve$f + rnorm(length(curve$f),sd=1) fast_fit = GP.fast.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE) reg_fit = GP.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE) sum_fast_fit = GP.summary(fast_fit) sum_reg_fit = GP.summary(reg_fit) curves = list(mean_fast = sum_fast_fit$mean, mean = sum_reg_fit$mean, lci_fast = sum_fast_fit$lci, lci = sum_reg_fit$lci, uci_fast = sum_fast_fit$uci, uci = sum_reg_fit$uci) GP.plot.curves(curves,layout=c(2,3))
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