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作者:Alan E. Gelfand , Erin M. Schliep
来源:[B].Bayesian Inference and Computing for Spatial Point Patterns2018Project Euclid
摘要:Contents 3.1 A general inference approach 3.2 Model adequacy and model comparison
作者:Hiroshi Kawakami
来源:[B].4-dimensional Painlevé-type equations2018Project Euclid
摘要:In this appendix, we provide a list of Hamiltonians for 4-dimensional Painlevé-type equations derived from the isomonodromic deformation of linear equations of ramified type (see Section 1.2 in Part 2).
作者:Alan E. Gelfand , Erin M. Schliep
来源:[B].Bayesian Inference and Computing for Spatial Point Patterns2018Project Euclid
摘要:Contents 1.1 What are spatial data? 1.2 Principles of Bayesian inference 1.3 Hierarchical modeling 1.4 Gaussian processes
作者:Alan E. Gelfand , Erin M. Schliep
来源:[B].Bayesian Inference and Computing for Spatial Point Patterns2018Project Euclid
摘要:Contents 2.1 Theory for spatial point patterns 2.2 Exploratory tools 2.3 Modeling λ ( s ) 2.4 General Cox processes 2.5 Inhibition processes 2.6 Marked point processes
作者:Alan E. Gelfand , Erin M. Schliep
来源:[B].Bayesian Inference and Computing for Spatial Point Patterns2018Project Euclid
摘要:Contents 5.1 Spatial modeling of presence-only data 5.2 Spatial modeling for presence/absence data using preferential sampling 5.3 Multivariate point patterns

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