Daniel Simpson – författare
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A succinct, approachable guide to the origins, development, key texts, concepts, and practices of yoga.Yoga is practiced by many millions of people worldwide and is celebrated for its mental, physical, and spiritual benefits. And yet, as Daniel Simpson reveals in The Truth of Yoga, much of what is said about yoga is misleading. For example, the word “yoga” does not always mean union. In fact, in perhaps the discipline’s most famous text—the Yoga Sutra of Patanjali—its aim is described as separation: isolating consciousness from everything else. And yoga is not five thousand years old, as is commonly claimed; the earliest evidence of practice dates back about twenty-five hundred years. (Yoga may well be older, but no one can prove it.)The Truth of Yoga is a clear, concise, and accessible handbook for the lay reader that draws upon abundant recent scholarship. It outlines these new findings with practitioners in mind, highlighting ways to keep traditions alive in the twenty-first century.
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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 Matérn 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.
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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 Matérn 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.
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