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Gaussian Markov Random Fields: Theory and Applications Havard Rue, Leonhard Held
Publisher: Chapman and Hall/CRC

Aug 9, 2011 - Markov random fields and graphical models are widely used to represent conditional independences in a given multivariate probability distribution (see [1–5], to name just a few). Rue H, Held L: Gaussian Markov Random Fields: Theory and Applications. London: Chapman & Hall/CRC Press; 2005. We present a novel empirical Bayes model called BayMeth, based on the Central Full Text OpenURL. Cartier, Bernard Julia, Pierre Moussa, Pierre Vanhove 2005 Springer 9783540231899,3-540-23189-7 . (Ed) 1974 Springer-Verlag 0-387-06752-3 Gaussian Markov Random Fields. Functional Analysis and Applications: Proceedings of the Symposium of Analysis Lecture notes in mathematics, 384 Nachbin L. Jul 6, 2013 - Frontiers in Number Theory, Physics and Geometry: On Random Matrices, Zeta Functions and Dynamical Systems Pierre Emile Cartier, Pierre E. Recently, in connection to Published in 2004 by Chapman and Hall/CRC, it provides a detailed account on the theory of spatial point process models and simulation-based inference as well as various application examples. Feb 11, 2014 - Very recently, a method based on combining profiles from MeDIP/MBD-seq and methylation-sensitive restriction enzyme sequencing for the same samples with a computational approach using conditional random fields appears promising [31]. Jul 5, 2008 - One of the most exciting recent developments in stochastic simulation is perfect (or exact) simulation, which turns out to be particularly applicable for most point process models and many Markov random field models as demonstrated in my work.