bcgpMCMC: Draw samples from a bcgp model
In cbdAmgen/bcgp0a: Bayesian Composite Gaussian Processes

Description Usage Arguments Details Value See Also Examples

bcgpMCMC draws samples from the Bayesian Composite Gaussian Process model

1 2	bcgpMCMC(x, y, priors, inits, numUpdates, numAdapt, burnin, nmcmc, chains = 4, cores = 1)

`x`	An `n x d` matrix containing the independent variables in the training set.
`y`	A vector containing the observed response values in the training set.
`numUpdates`	The number of updates in the proposal stepsize adaptation phase.
`numAdapt`	The number of samples within each update in the proposal stepsize adaptation phase.
`burnin`	The number of burnin samples to discard after the stepsize adaptation phase is finished
`nmcmc`	The number of samples to be kept for each Markov chain.
`chains`	A positive integer specifying the number of Markov chains. The default is 4.
`cores`	The number of cores to use when executing the Markov chains in parallel. The default is to use the value of the `mc.cores` option if it has been set and otherwise to default to 1 core.
`prior`	A list containing the values for the prior parameters.

This draws samples from the posterior distribution for the Bayesian Composite Gaussian Process (BCGP) model.

An object of S4 class bcgp representing the fitted results.

Other Major functions: bcgp

x <- matrix(runif(20, 0, 1), nrow = 10, ncol = 2)
y <- x[, 1] + sin(x[, 2])
priors <- createPriors(x, noise = FALSE)
inits <- createInits(x, priors, chains = 4)
numUpdates <- 3
numAdapt <- 500
burnin <- 500
nmcmc <- 5000
chains <- 4
cores <- 1
bcgpMCMC(x, y, priors, inits, numUpdates, numAdapt,
         burnin, nmcmc, chains, cores)