Statistical Inference for Spatial Processes

Ripley, Brian D.

ISBN 10: 0521424208 / ISBN 13: 9780521424202

Published by Cambridge University Press, 1991

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book introduces statistical inference in spatial statistics and its applications.InhaltsverzeichnisIntroduction 1. Likelihood analysis for spatial Gaussian processes 2. Edge correction for spatial point processes 3. Parameter. Seller Inventory # 446935029

Bibliographic Details

Title: Statistical Inference for Spatial Processes

Publisher: Cambridge University Press

Publication Date: 1991

Binding: Kartoniert / Broschiert

Condition: New

About this title

Synopsis:

This book is designed for specialists needing an introduction to statistical inference in spatial statistics and its applications. One of the author's themes is to show 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 the lack of a unique asymptotic setting in spatial problems. Throughout, he 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 summarizing of images.

From the Back Cover:

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 to show 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 the 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 summarizing 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.