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Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data

Spyropoulou, Maria‐Zafeiria, Bentham, James (2023) Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data. Statistica Neerlandica, . Article Number 12330. ISSN 0039-0402. E-ISSN 1467-9574. (doi:10.1111/stan.12330) (KAR id:103701)


An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods.

Item Type: Article
DOI/Identification number: 10.1111/stan.12330
Uncontrolled keywords: two‐dimensional data; MCMC; intrinsic Gaussian Markov random fields; scaling; precision; hyperpriors
Subjects: Q Science
Divisions: Divisions > Division of Natural Sciences > Sport and Exercise Sciences
Funders: University of Kent (
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 14 Mar 2024 14:38 UTC
Last Modified: 20 Mar 2024 15:54 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Spyropoulou, Maria‐Zafeiria.

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Bentham, James.

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