Bayesian regression modeling with INLA (Record no. 211400)

000 -LEADER
fixed length control field aam a22 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190418b 2018 ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781498727259
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.542
Item number W2B2
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Wang, Xiaofeng
9 (RLIN) 378580
245 ## - TITLE STATEMENT
Title Bayesian regression modeling with INLA
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2018
Place of publication, distribution, etc Florida
300 ## - PHYSICAL DESCRIPTION
Extent xii, 312p.
Other physical details With index
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Computer science and data analysis series
9 (RLIN) 378587
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of Contents
1.Introduction

2.Theory of INLA

3.Bayesian Linear Regression

4.Generalized Linear Models

5.Linear Mixed and Generalized Linear Mixed Models

6.Survival Analysis

7.Random Walk Models for Smoothing Methods

8.Gaussian Process Regression

9.Additive and Generalized Additive Models

10.Errors-in-Variables Regression

11.Miscellaneous Topics in INLA

Appendix A Installation
Appendix B Uninformative Priors in Linear Regression
520 ## - SUMMARY, ETC.
Summary, etc 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.

https://www.crcpress.com/Bayesian-Regression-Modeling-with-INLA/Wang-Ryan-Faraway/p/book/9781498727259
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Regression analysis
9 (RLIN) 378581
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bayesian statistical decision theory
9 (RLIN) 378582
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Gaussian processes
9 (RLIN) 378583
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Laplace transformation
9 (RLIN) 378584
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Yue, Yu Ryan
Relator term Co author
9 (RLIN) 378585
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Faraway, Julian J.
Relator term Co author
9 (RLIN) 378586
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Full call number Barcode Date last seen Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library General Stacks 2019-03-29 6 5.00 Slot 1677 (2 Floor, East Wing) 519.542 W2B2 198992 2019-03-29 7058.13 Books

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