Bayesian inference with INLA

The integrated nested Laplace approximation (INLA) is a fast computational method for fitting Bayesian models, focusing on marginal inference and exploiting conditional independence properties. It is described in Bayesian Inference with INLA, a book that covers model fitting for various applications with R, including generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Examples and datasets are provided, making it useful for researchers from diverse fields.

\n Format: Paperback / softback
\n Length: 332 pages
\n Publication date: 30 September 2021
\n Publisher: Taylor & Francis Ltd
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The integrated nested Laplace approximation (INLA) is a cutting-edge computational approach that significantly reduces the time needed to fit Bayesian models compared to traditional Markov chain Monte Carlo (MCMC) methods. INLA specializes in marginal inference on the parameters of latent Gaussian Markov random field models, leveraging conditional independence properties to achieve computational efficiency.

Bayesian Inference with INLA offers a comprehensive overview of INLA and its accompanying R package for model fitting. This book delves into the underlying methodology, providing step-by-step instructions on how to fit a diverse array of models using R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package, such as extending the number of priors and latent models available, are also explored.

All examples in the book are fully reproducible, and datasets and R code are readily accessible from the book's website. This book is invaluable for researchers from various fields, including biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences, who wish to employ the INLA method in their research endeavors. The examples encompass a wide range of applications, showcasing the versatility and effectiveness of INLA in addressing complex statistical problems.

\n Weight: 686g\n
Dimension: 178 x 254 x 21 (mm)\n
ISBN-13: 9781032174532\n \n