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Statistical Inference for Spatial Processes

Author: B. D. Ripley, University of Oxford

Date Published: July 1991
availability: Available
format: Paperback
isbn: 9780521424202

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Description

The study of spatial processes and their applications is an important topic in statistics and finds wide application particularly in computer vision and image processing. This book is devoted to statistical inference in spatial statistics and is intended for specialists needing an introduction to the subject and to its applications. One of the themes of the book is the demonstration of how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and pseudo-likelihood. Professor Ripley also stresses the importance of edge effects and of lack of a unique asymptotic setting in spatial problems. Throughout, the author discusses the foundational issues posed and the difficulties, both computational and philosophical, which arise. The final chapters consider image restoration and segmentation methods and the averaging and summarising of images. Thus, the book will find wide appeal to researchers in computer vision, image processing, and those applying microscopy in biology, geology and materials science, as well as to statisticians interested in the foundations of their discipline.
- New paperback
- Subject matter has wide applications - of particular note is that to computer vision and image processing
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Reviews & endorsements
' … this monograph is required reading for anyone interested in the theory of spatial processes.' Biometrics
'All in all, a beautiful introduction to this important area and highly recommended.' Mededelingen van het Wiskundig Genootschap
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Product details
- Date Published: July 1991
- format: Paperback
- isbn: 9780521424202
- length: 160 pages
- dimensions: 229 x 152 x 9 mm
- weight: 0.24kg
- contains: 50 b/w illus.
- availability: Available
Table of Contents
Introduction
1. Likelihood analysis for spatial Gaussian processes
2. Edge correction for spatial point processes
3. Parameter estimation for Gibbsian point processes
4. Modelling spatial images
5. Summarizing binary images.
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Author
B. D. Ripley, University of Oxford