Spatial Statistics and Computational Methods

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.