Elliptical slice sampling

TL;DR

This paper introduces elliptical slice sampling, a new MCMC method for efficient inference in models with Gaussian priors, offering simplicity, parameter-free operation, and broad applicability.

Contribution

It presents a novel, generic elliptical slice sampling algorithm that simplifies inference in Gaussian prior models without requiring parameter tuning.

Findings

Works well across various Gaussian process models

No free parameters needed for the sampling method

Simplifies inference process in Gaussian models

Abstract

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.