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Spatial modeling wi...
Spatial modeling with R-INLA: A review
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- Rue, H. (författare)
- King Abdullah University of Science and Technology (KAUST)
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- Fuglstad, G. A. (författare)
- Norges teknisk-naturvitenskapelige universitet (NTNU),Norwegian University of Science and Technology (NTNU)
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- Riebler, A. (författare)
- Norges teknisk-naturvitenskapelige universitet (NTNU),Norwegian University of Science and Technology (NTNU)
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- Bolin, David, 1983 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Chalmers tekniska högskola,Chalmers University of Technology
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- Illian, J. (författare)
- University of St Andrews
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- Krainski, E. (författare)
- Universidade Federal do Parana
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- Simpson, D. (författare)
- University of Toronto
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- Lindgren, F. (författare)
- University of Edinburgh
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- Bakka, Haakon (författare)
- King Abdullah University of Science and Technology (KAUST)
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(creator_code:org_t)
- 2018-07-05
- 2018
- Engelska.
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Ingår i: Wiley Interdisciplinary Reviews-Computational Statistics. - : Wiley. - 1939-0068 .- 1939-5108. ; 10:6
- Relaterad länk:
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http://eprints.gla.a...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Matern Gaussian random fields. In this review, we discuss the large success of spatial modeling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for nonstationary spatial models and nonseparable space-time models. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Bayesian Models Data: Types and Structure > Massive Data
Ämnesord
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- approximate Bayesian inference
- Gaussian Markov random fields
- Laplace approximations
- sparse
- approximate bayesian-inference
- point process models
- partial-differential-equations
- nested laplace approximation
- gaussian
- cox processes
- markov random-fields
- plasmodium-falciparum
- temporal
- analysis
- child-mortality
- soil properties
- stochastic partial differential equations
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Rue, H.
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Fuglstad, G. A.
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Riebler, A.
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Bolin, David, 19 ...
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Illian, J.
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Krainski, E.
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visa fler...
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Simpson, D.
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Lindgren, F.
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Bakka, Haakon
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Matematik
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Bioinformatik
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- NATURVETENSKAP
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NATURVETENSKAP
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och Matematik
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och Sannolikhetsteor ...
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
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och Reglerteknik
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Wiley Interdisci ...
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- Av lärosätet
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Göteborgs universitet
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Chalmers tekniska högskola