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The Integrated Nested Laplace Approximation (INLA) is a popular method for approximate Bayesian inference. This book provides an introduction to the underlying INLA methodology and practical guidance on how to fit different models with R-INLA and R. This covers a wide range of applications, such as multilevel models, spatial models and survival models, The book will also cover recent research on how to extend the types of models that can be fitted with INLA and R-INLA. This will include built-in features in R-INLA to define new latent models directly in R as well as combining INLA with numerical integration and MCMC methods.…mehr

Produktbeschreibung
The Integrated Nested Laplace Approximation (INLA) is a popular method for approximate Bayesian inference. This book provides an introduction to the underlying INLA methodology and practical guidance on how to fit different models with R-INLA and R. This covers a wide range of applications, such as multilevel models, spatial models and survival models, The book will also cover recent research on how to extend the types of models that can be fitted with INLA and R-INLA. This will include built-in features in R-INLA to define new latent models directly in R as well as combining INLA with numerical integration and MCMC methods.
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Autorenporträt
Virgilio Gómez-Rubio is associate professor in the Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, Albacete, Spain. He has developed several packages on spatial and Bayesian statistics that are available on CRAN, as well as co-authored books on spatial data analysis and INLA including Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA (CRC Press, 2019).