gradientGP | R Documentation |
Internal R function to pass R to C++, not for external use.
Internal R function to pass R to C++, not for external use.
Function to perform Metropolis-Hastings for GP hyperparameters with different priors
gradientGP(Xk, tau, h, nk, D) gradientGPmatern(Xk, tau, h, nk, D, materncov, nu) posteriorgradientGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar) gradientlogprior(h, hyppar) likelihoodGP(Xk, tau, h, nk, D) likelihoodGPmatern(Xk, tau, h, nk, D, materncov, nu) posteriorGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar) Gumbel(x, lambda, log = TRUE) PCrhomvar(rho, a, lambda1, lambda2, log = TRUE) metropolisGP( inith, X, tau, nk, D, niter, hyperMean = c(0, 0, 0), hyperSd = c(1, 1, 1) ) metropolisGPmatern( inith, X, tau, nk, D, niter, nu = 2, hyppar = c(1, 1, 1), propSd = c(0.3, 0.1, 0.1) ) Gumbel(x, lambda, log = TRUE) PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)
Xk |
The data |
tau |
The indexing parameters |
h |
GP hyperparameters |
nk |
Number of observations |
D |
number of samples |
materncov |
|
nu |
Smoothness of the matern covariance |
hyppar |
A vector indicating the penalised complexity prior hyperparameters.
Default is |
x |
observation |
lambda |
scale parameter of the type-2 Gumbel distribution |
log |
|
rho |
length-scale parameter |
a |
amplitude |
lambda1 |
first parameter of distribution |
lambda2 |
second parameter of distribution |
inith |
initial hyperparamters |
X |
The data |
niter |
Number of MH iteractions |
hyperMean |
A vector indicating the log-normal means. Default is |
hyperSd |
A vector indicating the log-normal standard deviations. Default is |
propSd |
The proposal standard deviation. Default is |
Returns gp gradient
Returns gp gradient
Returns the gradient of the posterior
return the gradient of the log prior, length-scale, aamplitude and noise
Returns gp negative log likelihood
Returns gp negative log likelihood
Returns the negative log posterior of the GP
Returns the likelihood of the type-2 GUmbel distribution
Returns the likelihood of the bivariate penalised complexity prior
Returns new hyperparamters and the acceptance rate
Returns the likelihood of the type-2 GUmbel distribution
Returns the likelihood of the bivariate penalised complexity prior
Gumbel(3, lambda = 1)
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