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Titlebook: Case Studies in Spatial Point Process Modeling; Adrian Baddeley,Pablo Gregori,Dietrich Stoyan Book 2006 Springer-Verlag New York 2006 Bran

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書(shū)目名稱(chēng)Case Studies in Spatial Point Process Modeling
編輯Adrian Baddeley,Pablo Gregori,Dietrich Stoyan
視頻videohttp://file.papertrans.cn/223/222285/222285.mp4
概述Focuses on case studies, rather than methodological aspects of point process modelling.Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Statistics
圖書(shū)封面Titlebook: Case Studies in Spatial Point Process Modeling;  Adrian Baddeley,Pablo Gregori,Dietrich Stoyan Book 2006 Springer-Verlag New York 2006 Bran
描述.Point process statistics is successfully used in fields such as material science, human epidemiology, social sciences, animal epidemiology, biology, and seismology. Its further application depends greatly on good software and instructive case studies that show the way to successful work. This book satisfies this need by a presentation of the spatstat package and many statistical examples...Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics. No other book presents so many well-founded point process case studies...From the reviews:.."For those interested in analyzing their spatial data, the wide variatey of examples and approaches here give a good idea of the possibilities and suggest reasonable paths to explore." .Michael Sherman?for the Journal of the American Statistical Association, December 2006.
出版日期Book 2006
關(guān)鍵詞Branching process; Markov property; Poisson process; point process; principal component analysis; statist
版次1
doihttps://doi.org/10.1007/0-387-31144-0
isbn_softcover978-0-387-28311-1
isbn_ebook978-0-387-31144-9Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag New York 2006
The information of publication is updating

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https://doi.org/10.1007/978-1-4612-4808-8something about how the two cell types emerge during development..Our goal in is to demonstrate how recently developed Monte Carlo methods for conducting likelihood-based analysis of realistic point process models can lead to sharper inferences about the bivariate structure of the data. In particula
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