Max-and-smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

DOI10.1214/20-BA1219zbMath1480.62056arXiv1907.11969OpenAlexW3035941548MaRDI QIDQ2057335

Haakon Bakka, Stefan Siegert, Raphaël Huser, Árni V. Jóhannesson, Birgir Hrafnkelsson

Publication date: 6 December 2021

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.11969

zbMATH Keywords

spatio-temporal dataBayesian hierarchical modelapproximate Bayesian inferencelatent Gaussian modelmultivariate link function

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Bayesian inference (62F15)

Related Items

Approximate Bayesian inference for analysis of spatiotemporal flood frequency data, A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Max-and-smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

Uses Software

• GMRFLib
• spBayes
• BayesDA
• FRK
• GPvecchia

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