Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques have each undergone major developments in the last decade. Also, these two areas are mutually reinforcing, because MCMC methods are often necessary for the practical implementation of spatial statistical inference, while new spatial stochastic models in turn motivate the development of improved MCMC algorithms. This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It consists of four chapters: 1. Petros Dellaportas and Gareth O. Roberts give a tutorial on MCMC methods, the computational methodology which is essential for virtually all the complex spatial models to be considered in subsequent chapters. 2. Peter J. Diggle, Paulo J, Ribeiro Jr., and Ole F. Christensen introduce the reader to the model- based approach to geostatistics, i.e. the application of general statistical principles to the formulation of explicit stochastic models for geostatistical data, and to inference within a declared class of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H?vard Rue discuss various aspects of image analysis, ranging from low to high level tasks, and illustrated with different examples of applications. 4. Jesper Moller and Rasmus P. Waggepetersen collect recent theoretical advances in simulation-based inference for spatial point processes, and discuss some examples of applications. The volume introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers. It is partly based on the course material for the "TMR and MaPhySto Summer School on Spatial Statistics and Computational Methods," held at Aalborg University, Denmark, August 19-22, 2001.
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Taschenbuch. Zustand: Neu. Neuware -Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques haveeach undergone major developments in the last decade. Also, these twoareas are mutually reinforcing, because MCMC methods are oftennecessary for the practical implementation of spatial statisticalinference, while new spatial stochastic models in turn motivate thedevelopment of improved MCMC algorithms.This volume shows how sophisticated spatial statistical andcomputational methods apply to a range of problems of increasingimportance for applications in science and technology. It consists offour chapters: 1. Petros Dellaportas and Gareth O. Roberts give atutorial on MCMC methods, the computational methodology which isessential for virtually all the complex spatial models to beconsidered in subsequent chapters. 2. Peter J. Diggle, Paulo JRibeiro Jr., and Ole F. Christensen introduce the reader to the modelbased approach to geostatistics, i.e. the application of generalstatistical principles to the formulation of explicit stochasticmodels for geostatistical data, and to inference within a declaredclass of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H vard Ruediscuss various aspects of image analysis, ranging from low to highlevel tasks, and illustrated with different examples of applications.4. Jesper Moller and Rasmus P. Waggepetersen collect recenttheoretical advances in simulation-based inference for spatial pointprocesses, and discuss some examples of applications.The volume introduces topics of current interest in spatial andcomputational statistics, which should be accessible to postgraduatestudents as well as to experienced statistical researchers. It ispartly based on the course material for the 'TMR and MaPhySto SummerSchool on Spatial Statistics and Computational Methods,' held atAalborg University, Denmark, August 19-22, 2001.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch. Artikel-Nr. 9780387001364
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques have each undergone major developments in the last decade. Also, these two areas are mutually reinforcing, because MCMC methods are often necessary for the practical implementation of spatial statistical inference, while new spatial stochastic models in turn motivate the development of improved MCMC algorithms. This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It consists of four chapters: 1. Petros Dellaportas and Gareth O. Roberts give a tutorial on MCMC methods, the computational methodology which is essential for virtually all the complex spatial models to be considered in subsequent chapters. 2. Peter J. Diggle, Paulo J, Ribeiro Jr., and Ole F. Christensen introduce the reader to the model- based approach to geostatistics, i.e. the application of general statistical principles to the formulation of explicit stochastic models for geostatistical data, and to inference within a declared class of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H vard Rue discuss various aspects of image analysis, ranging from low to high level tasks, and illustrated with different examples of applications. 4. Jesper Moller and Rasmus P. Waggepetersen collect recent theoretical advances in simulation-based inference for spatial point processes, and discuss some examples of applications. The volume introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers. It is partly based on the course material for the 'TMR and MaPhySto Summer School on Spatial Statistics and Computational Methods,' held at Aalborg University, Denmark, August 19-22, 2001. Artikel-Nr. 9780387001364
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