Statistical inference for spatial processes : an essay awarded the Adams Prize of the University of Cambridge

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. B.d. Ripley. Includes Index. Bibliography: P. [138]-145. 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. 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.

this Book Introduces Statistical Inference In Spatial Statistics And Its Applications.

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on Statistical Inference In Spatial Statistics And Its Applications (in Computer Vision, Image Processing, Microscopy). Ripley Shows How These Techniques Give New Insights Into Classical Procedures And Newer Statistical Paradigms. He Also Stresses The Importance Of Edge Effects And Of The Lack Of A Unique Asymptotic Setting In Spatial Problems. The Essay Discusses The Foundational Issues Posed And The Difficulties, Both Computational And Philosophical, Which Arise. Acidic Paper. Annotation C. Book News, Inc., Portland, Or (booknews.com)

This essay aims to bring out some of the distinctive features and special problems of statistical inference on spatial processes.