文章基本信息

标题：Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples
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作者：Thomas J. Leininger ; Alan E. Gelfand
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2017
卷号：12
期号：1
页码：1-30
DOI：10.1214/15-BA985
语种：English
出版社：International Society for Bayesian Analysis
摘要：Spatial point pattern data describes locations of events observed over a given domain, with the number of and locations of these events being random. Historically, data analysis for spatial point patterns has focused on rejecting complete spatial randomness and then on fitting a richer model specification. From a Bayesian standpoint, the literature is growing but primarily considers versions of Poisson processes, focusing on specifications for the intensity. However, the Bayesian literature on, e.g., clustering or inhibition processes is limited, primarily attending to model fitting. There is little attention given to full inference and scant with regard to model adequacy or model comparison.