ess: Posterior Sampling Using Elliptical Slice Sampling

In FastGP: Efficiently Using Gaussian Processes with Rcpp and RcppEigen

Posterior Sampling Using Elliptical Slice Sampling

Description

The ess function uses elliptical slice sampling to sample from the posterior distribution within a Bayesian model in which the prior is a Gaussian process/multivariate normal. If the covariance matrix corresponding to the prior is also Toeplitz, use ess_Toep for a faster version.

Elliptical slice sampling is a valid Markov chain Monte Carlo algorithm for posterior sampling when the prior is a Gaussian process/multivariate normal.

Murray, I., Adams, R., & MacKay, D. (2010, March). Elliptical slice sampling. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 541-548). JMLR Workshop and Conference Proceedings.

Usage

ess(log.lik,Y, Sig, N_mcmc,burn_in,N,flag)
ess_Toep(log.lik,Y, Sig, N_mcmc,burn_in,N)

Arguments

log.lik	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.
Y	Observed data.
Sig	Covariance matrix associated with the prior distribution on the parameters/latent variable vector.
N_mcmc	Number of desired mcmc samples.
burn_in	Number of burn-in iterations.
N	Dimensionality of parameter/latent variable vector.
flag	Set to TRUE for MASS implementation of mvrnorm, FALSE for FastGP implementation of rcpp_rmvnorm.

Author(s)

Giri Gopalan [e-mail]

Examples

# See demo/FastGPdemo.r.

FastGP documentation built on Sept. 20, 2026, 9:06 a.m.