A determinant-free method to simulate the parameters of large Gaussian fields

Louis Ellam, Heiko Strathmann, Mark Girolami, Iain Murray

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We propose a determinant-free approach for simulation-based Bayesian inference in high-dimensional Gaussian models. We introduce auxiliary variables with covariance equal to the inverse covariance of the model. The joint
probability of the auxiliary model can be computed without evaluating determinants, which are often hard to compute in high dimensions. We develop a Markov chain Monte Carlo sampling scheme for the auxiliary model that requires no more than the application of inverse-matrix-square-roots and the solution of linear systems. These operations can be performed at large scales with rational approximations. We provide an empirical study on both synthetic and real-world data for sparse Gaussian processes and for large-scale Gaussian Markov random fields.
Original languageEnglish
Pages (from-to)271-281
Number of pages11
JournalStat
Volume6
Issue number1
Early online date15 Aug 2017
DOIs
Publication statusPublished - Aug 2017

Fingerprint

Dive into the research topics of 'A determinant-free method to simulate the parameters of large Gaussian fields'. Together they form a unique fingerprint.

Cite this