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The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models

Boom, Willem van den; Beskos, Alexandros; Iorio, Maria De; (2022) The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models. Journal of Computational and Graphical Statistics , 31 (4) pp. 1215-1224. 10.1080/10618600.2022.2050250. Green open access

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Abstract

Gaussian graphical models can capture complex dependency structures amongst variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate prior information and to quantify uncertainty through the posterior distribution. However, posterior computation under the conjugate G-Wishart prior distribution on the precision matrix is expensive for general non-decomposable graphs. We therefore propose a new Markov chain Monte Carlo (MCMC) method named the G-Wishart weighted proposal algorithm (WWA). WWA's distinctive features include delayed acceptance MCMC, Gibbs updates for the precision matrix and an informed proposal distribution on the graph space that enables embarrassingly parallel computations. Compared to existing approaches, WWA reduces the frequency of the relatively expensive sampling from the G-Wishart distribution. This results in faster MCMC convergence, improved MCMC mixing and reduced computation time. Numerical studies on simulated and real data show that WWA provides a more efficient tool for posterior inference than competing state-of-the-art MCMC algorithms.

Type: Article
Title: The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/10618600.2022.2050250
Publisher version: https://doi.org/10.1080/10618600.2022.2050250
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Exchange algorithm, Hyper Inverse Wishart distribution, Locally balanced proposal, Reversible jump MCMC, Scalable Bayesian computations
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10144506
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