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Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
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Bayesian inference with INLA (Paperback)
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Bayesian inference with INLA (Paperback)
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The integrated nested Laplace approximation (INLA) is a recent
computational method that can fit Bayesian models in a fraction of
the time required by typical Markov chain Monte Carlo (MCMC)
methods. INLA focuses on marginal inference on the model parameters
of latent Gaussian Markov random fields models and exploits
conditional independence properties in the model for computational
speed. Bayesian Inference with INLA provides a description of INLA
and its associated R package for model fitting. This book describes
the underlying methodology as well as how to fit a wide range of
models with 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 and how to extend the number of priors and
latent models available in the package are discussed. All examples
in the book are fully reproducible and datasets and R code are
available from the book website. This book will be helpful to
researchers from different areas with some background in Bayesian
inference that want to apply the INLA method in their work. The
examples cover topics on biostatistics, econometrics, education,
environmental science, epidemiology, public health, and the social
sciences.
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