Gaussian Markov random fields. Theory and applications. Researchers in spatial statistics and image analysis are familiar with Gaussian Markov Random Fields (GMRFs), and they are traditionally among the few who use them. There are, however, a wide range of applications for this methodology, from structural time-series analysis to the analysis of longitudinal and survival data, spatio-temporal models, graphical models, and semi-parametric statistics. With so many applications and with such widespread use in the field of spatial statistics, it is surprising that there remains no comprehensive reference on the subject.par Gaussian Markov Random Fields: Theory and Applications provides such a reference, using a unified framework for representing and understanding GMRFs. Various case studies illustrate the use of GMRFs in complex hierarchical models, in which statistical inference is only possible using Markov Chain Monte Carlo (MCMC) techniques. The preeminent experts in the field, the authors emphasize the computational aspects, construct fast and reliable algorithms for MCMC inference, and provide an online C-library for fast and exact simulation.par This is an ideal tool for researchers and students in statistics, particularly biostatistics and spatial statistics, as well as quantitative researchers in engineering, epidemiology, image analysis, geography, and ecology, introducing them to this powerful statistical inference method.

References in zbMATH (referenced in 250 articles , 1 standard article )

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  1. Dolgov, Sergey; Anaya-Izquierdo, Karim; Fox, Colin; Scheichl, Robert: Approximation and sampling of multivariate probability distributions in the tensor train decomposition (2020)
  2. Ip, Ryan H. L.; Wu, K. Y. K.: A note on discrete multivariate Markov random field models (2020)
  3. Žukovič, Milan; Borovský, Michal; Lach, Matúš; Hristopulos, Dionissios T.: GPU-accelerated simulation of massive spatial data based on the modified planar rotator model (2020)
  4. Abbruzzo, Antonino; Vujačić, Ivan; Mineo, Angelo M.; Wit, Ernst C.: Selecting the tuning parameter in penalized Gaussian graphical models (2019)
  5. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  6. Barthelmé, Simon; Amblard, Pierre-Olivier; Tremblay, Nicolas: Asymptotic equivalence of fixed-size and varying-size determinantal point processes (2019)
  7. Castro-Camilo, Daniela; Huser, Raphaël; Rue, Håvard: A spliced gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting (2019)
  8. Gopalan, Giri; Hrafnkelsson, Birgir; Wikle, Christopher K.; Rue, Håvard; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur: A hierarchical spatiotemporal statistical model motivated by glaciology (2019)
  9. Ickowicz, Adrien; Ford, Jessica; Hayes, Keith: A mixture model approach for compositional data: inferring land-use influence on point-referenced water quality measurements (2019)
  10. Junker, Philipp; Nagel, Jan: A relaxation approach to modeling the stochastic behavior of elastic materials (2019)
  11. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  12. Metzner, Selma; Wübbeler, Gerd; Elster, Clemens: Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging (2019)
  13. Prates, Marcos Oliveira; Assunção, Renato Martins; Rodrigues, Erica Castilho: Alleviating spatial confounding for areal data problems by displacing the geographical centroids (2019)
  14. Risser, Mark D.; Paciorek, Christopher J.; Stone, Dáithí A.: Spatially dependent multiple testing under model misspecification, with application to detection of anthropogenic influence on extreme climate events (2019)
  15. Roininen, Lassi; Girolami, Mark; Lasanen, Sari; Markkanen, Markku: Hyperpriors for Matérn fields with applications in Bayesian inversion (2019)
  16. Saibaba, Arvind K.; Bardsley, Johnathan; Brown, D. Andrew; Alexanderian, Alen: Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems (2019)
  17. Schmich, Fabian; Kuipers, Jack; Merdes, Gunter; Beerenwinkel, Niko: netprioR: a probabilistic model for integrative hit prioritisation of genetic screens (2019)
  18. Schnell, Patrick M.; Bose, Maitreyee: Spectral parameterization for linear mixed models applied to confounding of fixed effects by random effects (2019)
  19. Sørbye, Sigrunn H.; Myrvoll-Nilsen, Eirik; Rue, Håvard: An approximate fractional Gaussian noise model with (\mathcalO(n)) computational cost (2019)
  20. Thaden, Hauke; Klein, Nadja; Kneib, Thomas: Multivariate effect priors in bivariate semiparametric recursive Gaussian models (2019)

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