GMRFLib

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 178 articles , 1 standard article )

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  1. Han, Insu; Malioutov, Dmitry; Avron, Haim; Shin, Jinwoo: Approximating spectral sums of large-scale matrices using stochastic Chebyshev approximations (2017)
  2. Held, Leonhard; Sauter, Rafael: Adaptive prior weighting in generalized regression (2017)
  3. Parton, A.; Blackwell, P.G.: Bayesian inference for multistate `step and turn’ animal movement in continuous time (2017)
  4. Wang, Hui; Wellmann, J.Florian; Li, Zhao; Wang, Xiangrong; Liang, Robert Y.: A segmentation approach for stochastic geological modeling using hidden Markov random fields (2017)
  5. Boojari, Hossein; Khaledi, Majid Jafari; Rivaz, Firoozeh: A non-homogeneous skew-gaussian Bayesian spatial model (2016)
  6. Byrd, Richard H.; Nocedal, Jorge; Oztoprak, Figen: An inexact successive quadratic approximation method for L-1 regularized optimization (2016)
  7. Fox, Colin; Norton, Richard A.: Fast sampling in a linear-Gaussian inverse problem (2016)
  8. Giscard, P.-L.; Choo, Z.; Thwaite, S.J.; Jaksch, D.: Exact inference on Gaussian graphical models of arbitrary topology using path-sums (2016)
  9. Mozumder, Meghdoot; Tarvainen, Tanja; Arridge, Simon; Kaipio, Jari P.; D’Andrea, Cosimo; Kolehmainen, Ville: Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography (2016)
  10. Rougier, Jonathan; Zammit-Mangion, Andrew: Visualization for large-scale Gaussian updates (2016)
  11. Solonen, Antti; Cui, Tiangang; Hakkarainen, Janne; Marzouk, Youssef: On dimension reduction in Gaussian filters (2016)
  12. Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
  13. Zareifard, Hamid; Rue, Håvard; Khaledi, Majid Jafari; Lindgren, Finn: A skew Gaussian decomposable graphical model (2016)
  14. Antonio, Katrien; Bardoutsos, Anastasios; Ouburg, Wilbert: Bayesian Poisson log-bilinear models for mortality projections with multiple populations (2015)
  15. Bachl, Fabian E.; Lenkoski, Alex; Thorarinsdottir, Thordis L.; Garbe, Christoph S.: Bayesian motion estimation for dust aerosols (2015)
  16. Bardsley, Johnathan M.; Luttman, Aaron: Dealing with boundary artifacts in MCMC-based deconvolution (2015)
  17. Barthelmé, Simon: Fast matrix computations for functional additive models (2015)
  18. Ekheden, Erland; Hössjer, Ola: Multivariate time series modeling, estimation and prediction of mortalities (2015)
  19. Gower, Robert M.; Richtárik, Peter: Randomized iterative methods for linear systems (2015)
  20. Kang, Su Yun; McGree, James; Baade, Peter; Mengersen, Kerrie: A case study for modelling cancer incidence using Bayesian spatio-temporal models (2015)

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