Daniel Simpson

Succinct answers to nearly any question about yoga's origins, development, key texts, concepts, and practices Much of what is said about yoga is misleading. To take two examples, it is neither five thousand years old, as is commonly claimed, nor does it mean union, at least not exclusively. In perhaps the most famous text—The Yoga Sutras of Patanjali—the aim is separation, isolating consciousness from everything else. And the earliest evidence of practice dates back about twenty-five hundred years. Yoga may well be older, but no one can prove it. Scholars have learned a lot more about the history of yoga in recent years, but their research can be hard to track down. Although their work is insightful, it is aimed more at specialists than at general readers. Daniel Simpson's The Truth of Yoga draws on many of their findings, presented in a format designed for practitioners. The aim is to highlight ideas on which readers can draw to keep traditions alive in the twenty-first century. It offers an overview of yoga's evolution from its earliest origins to the present. It can either be read chronologically or be used as a reference guide to history and philosophy. Each short section addresses one element, quoting from traditional texts and putting their teachings into context. The intention is to keep things clear without oversimplifying.

A Comprehensive Guide to Yoga's History, Texts, Philosophy, and Practices

The Truth of Yoga

Simpson, stationed in Belgrade, quits his reporting job for the New York Times and finds himself organizing Serbia's version of Woodstock with the help and hindrance of the criminal underworld.

A Rough Guide to the Dark Side

Virgilio Gómez-Rubio

Haakon Bakka

Amanda Lenzi

Daniela Castro-Camilo

Finn Lindgren

Håvard Rue

E. T. Krainski

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 biostatisties, 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. Book jacket.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and Inla

Elias T. Krainski

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.

Daniel Simpson

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