Bayesian regression modeling with INLA
著者
書誌事項
Bayesian regression modeling with INLA
（Series in computer science and data analysis）（A Chapman & Hall book）
CRC Press, c2018
- : hardback
- タイトル別名
-
Bayesian regression modeling with integrated Laplace approximation
大学図書館所蔵 全4件
注記
Includes bibliographical references (p. 297-308) and index
内容説明・目次
内容説明
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.
目次
Introduction to Bayesian Statistics. Bayesian Hierarchical Modeling. Model-Based Bayesian Inference. Linear and Generalized Linear Models. Linear and Generalized Linear Mixed Models. Zero-Inflated Mixture Models. Survival Analysis. Nonparametric Regression and Additive Models. Functional Regression Models. Measurement Error Models. Quantile Regression.
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