Sampling Strategies for Fast Updating of Gaussian Markov Random Fields.
Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jo...
| Publicado en: | American Statistician Vol. 75; no. 1; pp. 52 - 66 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Feb2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=148278886&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 148278886 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2021 vid: 75 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 148278886 10.1080/00031305.2019.1595144 ppf: 52 ppct: 14 formats: tig: atl: Sampling Strategies for Fast Updating of Gaussian Markov Random Fields. aug: au: Brown, D. Andrew McMahan, Christopher S. Watson Self, Stella affil: School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC su: Gaussian Markov random fields Random matrices Random variables Markov chain Monte Carlo Sparse matrices Gibbs sampling Markov random fields sug: subj: Gaussian Markov random fields Random matrices Random variables Markov chain Monte Carlo Sparse matrices Gibbs sampling Markov random fields keyword: Bayesian computation Cholesky factorization Chromatic Gibbs sampling Conditional autoregressive model Graph coloring Bayesian computation Cholesky factorization Chromatic Gibbs sampling Conditional autoregressive model Graph coloring ab: Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jointly in a block Gibbs sampler or componentwise in a single-site sampler via the full conditional distributions. The former approach can speed convergence by updating correlated variables all at once, while the latter avoids solving large matrices. We consider a sampling approach in which the underlying graph can be cut so that conditionally independent sites are updated simultaneously. This algorithm allows a practitioner to parallelize updates of subsets of locations or to take advantage of "vectorized" calculations in a high-level language such as R. Through both simulated and real data, we demonstrate computational savings that can be achieved versus both single-site and block updating, regardless of whether the data are on a regular or an irregular lattice. The approach provides a good compromise between statistical and computational efficiency and is accessible to statisticians without expertise in numerical analysis or advanced computing. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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