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Statistical Inference for Spatial Processes 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Statistical Inference for Spatial Processes
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B. D. Ripley 作者
Cambridge University Press
譯者
1991-07-26 出版日期
160 頁數
USD 37.99 價格
Paperback
叢書系列
9780521424202 圖書編碼

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Statistical Inference for Spatial Processes 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Statistical Inference for Spatial Processes 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Statistical Inference for Spatial Processes 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Statistical Inference for Spatial Processes 在線電子書 用戶評價

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評分☆☆☆☆☆

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評分☆☆☆☆☆

He said I might not understand the whole book even after I'd finished my PhD. Let's see...

評分☆☆☆☆☆

He said I might not understand the whole book even after I'd finished my PhD. Let's see...

Statistical Inference for Spatial Processes 在線電子書 著者簡介

Statistical Inference for Spatial Processes 在線電子書 著者簡介

Statistical Inference for Spatial Processes 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Statistical Inference for Spatial Processes 在線電子書 圖書描述

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.

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