Bayesian Regression Modeling with INLA

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A01=Julian J. Faraway
A01=Xiaofeng Wang
A01=Yu Ryan Yue
A01=Yu Yue Ryan
Accelerated Failure Time Models
advanced Bayesian regression applications
Age Group_Uncategorized
Age Group_Uncategorized
Applied data analysis
Approximate Bayesian inference
Author_Julian J. Faraway
Author_Xiaofeng Wang
Author_Yu Ryan Yue
Author_Yu Yue Ryan
automatic-update
Bayesian hierarchical models
Bayesian Residual
Beta Regression Model
Category1=Non-Fiction
Category=PBT
COP=United States
Cox Snell Residual Plots
Cox Snell Residuals
Credible Band
Credible Interval
Delivery_Delivery within 10-20 working days
eq_isMigrated=2
eq_nobargain
generalized linear models
GMRF
GPR Model
INLA Method
Integrated nested Laplace approximations
Julian J. Faraway
Language_English
Laplace Approximation
Latent Gaussian Model
Main Effect Function
Martingale Residuals
MCMC Computation
MCMC Simulation
measurement error correction
Measurement Error Model
Negative Binomial Model
Negative Hessian Matrix
nonparametric regression techniques
Normal Inverse Gaussian
PA=Available
Precision Matrix
Price_€50 to €100
PS=Active
R computation
Regression Model
RW2 Model
Small DIC
softlaunch
statistical computing in R
survival data analysis
Yuryan Yue
ZINB Model

Product details

  • ISBN 9781498727259
  • Weight: 600g
  • Dimensions: 156 x 234mm
  • Publication Date: 16 Feb 2018
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference.

Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download.

The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.

Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.

Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.

Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.

Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.

Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

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