bcgp: Draw samples from a bcgp model
In cbdavis33/bcgp1: Bayesian Composite Gaussian Processes

Description Usage Arguments Details Value See Also Examples

bcgp draws samples from the Bayesian Composite Gaussian Process model

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bcgp(xTrain, yTrain, priors = "default", inits = "random",
  numUpdates = 5, numAdapt = 1000, burnin = 1000, nmcmc = 10000,
  chains = 4, cores = getOption("mc.cores", 1L), noise = FALSE)

`xTrain`	An `n x d` matrix containing the independent variables in the training set.
`yTrain`	A vector containing the observed response values in the training set.
`priors`	Can be either the string "default" or a list containing the values for the prior parameters. `priors = "default"` (default): The priors are given default values. `priors` via list: Set prior values by providing a list equal in length to the number of Markov chains. A call to `createPriors()` will assist in the correct creation of this list.
`inits`	Can be either the string "random" or a list of length `chains`. The elements of this list should be named lists, where each of these has the name of a parameter and is used to specify the initial values for that parameter for the corresponding chain. `inits = "random"` (default): The initial values will be generated randomly from their respective prior distributions. `inits` via list: Set initial values by providing a list equal in length to the number of Markov chains. A call to `createInits()` will assist in the correct creation of this list.
`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`	The number of Markov chains. The default is 4.

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.

createPriors createInits

Other Major functions: bcgpMCMC

xTrain <- matrix(runif(20, 0, 10), nrow = 10, ncol = 2)
yTrain <- xTrain[, 1] + sin(xTrain[, 2])
priors <- createPriors(noise = FALSE, d = 2)
bcgp(xTrain, yTrain, priors)