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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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0930-0325 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 2006.978-0-387-28311-1978-0-387-31144-9Series ISSN 0930-0325 Series E-ISSN 2197-7186
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https://doi.org/10.1007/978-1-4612-4138-6e a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the “unknown” likelihood, perfect simulation algorithms for spatial point processes become useful.
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Office Ultrasonography of the Pelvis, spatially aggregated data depending on covariates. We fit initially a non-spatial version of the model to the doctor-prescribing data. We then consider spatial dependence in the data after the large-scale variation has been accounted for, and propose a final model that explains doctors’ prescribing patterns.
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Bayesian Analysis of Markov Point Processese a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the “unknown” likelihood, perfect simulation algorithms for spatial point processes become useful.
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0930-0325 stics 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
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Interpretation of object-oriented languagescale factor. The main emphasis of the present paper is on analysis of such models. Statistical methods are developed for estimation of scaling function and template parameters as well as for model validation. The proposed methods are assessed by simulation and used in the analysis of a vegetation pattern.
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