Log-lik function in model which is assumed to take two arguments: the first contains the parameters/latent variables and the second the observed data Y

log.lik

Covariance matrix associated with the prior distribution on the parameters/latent variable vector. 

N_mcmc

burn_in

Dimensionality of parameter/latent variable vector.

Set to TRUE for MASS implementation of mvrnorm (which may be more stable but slow), FALSE for FastGP implementation of rcpp_rmvnorm (which is faster but less stable)

flag


  This function uses elliptical slice sampling to sample from a Bayesian model in which the prior is multivariate normal (JMLR Murray, Adams, and MacKay 2010)

Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).

ess: Sampling from a Bayesian model with a multivariate normal prior distribution

Description

Usage

Arguments

Examples