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عنوان
Advanced spatial modeling with stochastic partial differential equations using R and INLA /

پدید آورنده
Elias Krainski [and seven others].

موضوع
Laplace transformation.,Mathematical models.,R (Computer program language),Stochastic differential equations.,Stochastic processes.,Laplace transformation.,Mathematical models.,MATHEMATICS / Applied,MATHEMATICS / Probability & Statistics / General,R (Computer program language),Stochastic differential equations.,Stochastic processes.

رده
QA274
.
23
.
K73
2019eb

کتابخانه
کتابخانه مطالعات اسلامی به زبان های اروپایی

محل استقرار
استان: قم ـ شهر: قم

کتابخانه مطالعات اسلامی به زبان های اروپایی

تماس با کتابخانه : 32910706-025

0429031890
0429626576
0429628218
0429629850
9780429031892
9780429626579
9780429628214
9780429629853
1138369853
9781138369856

Advanced spatial modeling with stochastic partial differential equations using R and INLA /
[Book]
Elias Krainski [and seven others].

Boca Raton, FL :
CRC Press, Taylor & Francis Group,
[2019]

1 online resource (xiii, 283 pages)

Includes bibliographical references and index.

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matrn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Taylor & Francis
9780429031892

Advanced spatial modeling with stochastic partial differential equations using R and INLA.
9781138369856

Laplace transformation.
Mathematical models.
R (Computer program language)
Stochastic differential equations.
Stochastic processes.
Laplace transformation.
Mathematical models.
MATHEMATICS / Applied
MATHEMATICS / Probability & Statistics / General
R (Computer program language)
Stochastic differential equations.
Stochastic processes.

MAT-- 003000
MAT-- 029000
PBT

519
.
2/2
23

QA274
.
23
.
K73
2019eb

Krainski, E. T., (Elias T.)

20200822162305.0
pn

 مطالعه متن کتاب 

[Book]

Y

الاقتراح / اعلان الخلل

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