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Journal of the American Statistical Association Volume 117, 2022 - Issue 538
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Theory and Methods

Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains

Michele Peruzzia Department of Forestry, Michigan State University, East Lansing, MI;b Department of Statistical Science, Duke University, Durham, NCORCID Iconhttps://orcid.org/0000-0003-4242-8059
ORCID Icon,
Sudipto Banerjeec Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CACorrespondence[email protected]
&
Andrew O. Finleya Department of Forestry, Michigan State University, East Lansing, MI
Pages 969-982 | Received 26 Mar 2020, Accepted 02 Oct 2020, Published online: 24 Nov 2020

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Cheng Li. (2022) Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model. The Annals of Statistics 50:6.
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Lu Zhang, Sudipto Banerjee & Andrew O. Finley. (2021) High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach. Environmetrics 32:4.
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