"Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA" - Information and Links:

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA - Info and Reading Options

"Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA" was published by Taylor & Francis Group in 2018 - Milton, it has 284 pages and the language of the book is English.


“Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA” Metadata:

  • Title: ➤  Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
  • Authors:
  • Language: English
  • Number of Pages: 284
  • Publisher: Taylor & Francis Group
  • Publish Date:
  • Publish Location: Milton

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"Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA" Description:

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Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- What this book is and isn't -- 1: The Integrated Nested Laplace Approximation and the R-INLA package -- 1.1 Introduction -- 1.2 The INLA method -- 1.3 A simple example -- 1.4 Additional arguments and control options -- 1.5 Manipulating the posterior marginals -- 1.6 Advanced features -- 2: Introduction to spatial modeling -- 2.1 Introduction -- 2.2 The SPDE approach -- 2.3 A toy example -- 2.4 Projection of the random field -- 2.5 Prediction -- 2.6 Triangulation details and examples -- 2.7 Tools for mesh assessment -- 2.8 Non-Gaussian response: Precipitation in Paraná -- 3: More than one likelihood -- 3.1 Coregionalization model -- 3.2 Joint modeling: Measurement error model -- 3.3 Copying part of or the entire linear predictor -- 4: Point processes and preferential sampling -- 4.1 Introduction -- 4.2 Including a covariate in the log-Gaussian Cox process -- 4.3 Geostatistical inference under preferential sampling -- 5: Spatial non-stationarity -- 5.1 Explanatory variables in the covariance -- 5.2 The Barrier model -- 5.3 Barrier model for noise data in Albacete (Spain) -- 6: Risk assessment using non-standard likelihoods -- 6.1 Survival analysis -- 6.2 Models for extremes -- 7: Space-time models -- 7.1 Discrete time domain -- 7.2 Continuous time domain -- 7.3 Lowering the resolution of a spatio-temporal model -- 7.4 Conditional simulation: Combining two meshes -- 8: Space-time applications -- 8.1 Space-time coregionalization model -- 8.2 Dynamic regression example -- 8.3 Space-time point process: Burkitt example -- 8.4 Large point process dataset -- 8.5 Accumulated rainfall: Hurdle Gamma model -- A: List of symbols and notation -- B: Packages used in the book -- Bibliography -- Index

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