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 261 articles , 1 standard article )

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  1. Bachoc, F.; Lagnoux, A.: Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes (2020)
  2. Bardsley, Johnathan M.; Hansen, Per Christian: MCMC algorithms for computational UQ of nonnegativity constrained linear inverse problems (2020)
  3. Barzegar, Zahra; Rivaz, Firoozeh: A scalable Bayesian nonparametric model for large spatio-temporal data (2020)
  4. Dolgov, Sergey; Anaya-Izquierdo, Karim; Fox, Colin; Scheichl, Robert: Approximation and sampling of multivariate probability distributions in the tensor train decomposition (2020)
  5. Ip, Ryan H. L.; Wu, K. Y. K.: A note on discrete multivariate Markov random field models (2020)
  6. Kirsner, Daniel; Sansó, Bruno: Multi-scale shotgun stochastic search for large spatial datasets (2020)
  7. Monterrubio-Gómez, Karla; Roininen, Lassi; Wade, Sara; Damoulas, Theodoros; Girolami, Mark: Posterior inference for sparse hierarchical non-stationary models (2020)
  8. Žukovič, Milan; Borovský, Michal; Lach, Matúš; Hristopulos, Dionissios T.: GPU-accelerated simulation of massive spatial data based on the modified planar rotator model (2020)
  9. Abbruzzo, Antonino; Vujačić, Ivan; Mineo, Angelo M.; Wit, Ernst C.: Selecting the tuning parameter in penalized Gaussian graphical models (2019)
  10. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  11. Barthelmé, Simon; Amblard, Pierre-Olivier; Tremblay, Nicolas: Asymptotic equivalence of fixed-size and varying-size determinantal point processes (2019)
  12. Castro-Camilo, Daniela; Huser, Raphaël; Rue, Håvard: A spliced gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting (2019)
  13. 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)
  14. Ickowicz, Adrien; Ford, Jessica; Hayes, Keith: A mixture model approach for compositional data: inferring land-use influence on point-referenced water quality measurements (2019)
  15. Junker, Philipp; Nagel, Jan: A relaxation approach to modeling the stochastic behavior of elastic materials (2019)
  16. Lehnert, Judith; Kolbitsch, Christoph; Wübbeler, Gerd; Chiribiri, Amedeo; Schaeffter, Tobias; Elster, Clemens: Large-scale Bayesian spatial-temporal regression with application to cardiac MR-perfusion imaging (2019)
  17. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  18. Metzner, Selma; Wübbeler, Gerd; Elster, Clemens: Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging (2019)
  19. Prates, Marcos Oliveira; Assunção, Renato Martins; Rodrigues, Erica Castilho: Alleviating spatial confounding for areal data problems by displacing the geographical centroids (2019)
  20. 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)

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