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Full Description
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.
Contents
1 An Introduction to MCMC.- 2 An Introduction to Model-Based Geostatistics.- 3 A Tutorial on Image Analysis.- 4 An Introduction to Simulation-Based Inference for Spatial Point Processes.
