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  3. Epidemiologi og medicinsk statistik

Bayesian inference with INLA

Af: Virgilio Gomez-Rubio Engelsk Hardback

Bayesian inference with INLA

Af: Virgilio Gomez-Rubio Engelsk Hardback
Tjek vores konkurrenters priser

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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 316
ISBN-13: 9781138039872
Indbinding: Hardback
Udgave:
ISBN-10: 113803987X
Kategori: Epidemiologi og medicinsk statistik
Udg. Dato: 17 feb 2020
Længde: 27mm
Bredde: 260mm
Højde: 184mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 17 feb 2020
Forfatter(e): Virgilio Gomez-Rubio
Forfatter(e) Virgilio Gomez-Rubio

Kategori Epidemiologi og medicinsk statistik

ISBN-13 9781138039872

Sprog Engelsk

Indbinding Hardback

Sider 316

Udgave

Længde 27mm

Bredde 260mm

Højde 184mm

Udg. Dato 17 feb 2020

Oplagsdato 17 feb 2020

Forlag Taylor & Francis Ltd

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