Angielski
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 J,Ribeiro Jr., and Ole F. Christensen introduce the reader to the model-based 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.
