skip to content
Bayesian regression modeling with INLA Preview this item
ClosePreview this item
Checking...

Bayesian regression modeling with INLA

Author: Xiaofeng Wang, (Professor of medicine); Yu Yue; Julian James Faraway
Publisher: Boca Raton : CRC Press, 2018.
Series: Chapman & Hall/CRC computer science and data analysis series
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms,  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Additional Physical Format: ebook version :
Document Type: Book
All Authors / Contributors: Xiaofeng Wang, (Professor of medicine); Yu Yue; Julian James Faraway
ISBN: 9781498727259 1498727255
OCLC Number: 1008765295
Notes: Includes index.
Description: xii, 312 pages ; 24 cm
Contents: 1.Introduction Quick Start Hubble's Law Standard Analysis Bayesian Analysis INLA Bayes Theory Prior and Posterior Distributions Model Checking Model Selection Hypothesis testing Bayesian Computation Exact Sampling Approximation 2.Theory of INLA Latent Gaussian Models (LGMs) Gaussian Markov Random Fields (GMRFs) Laplace Approximation and INLA INLA Problems Extensions 3.Bayesian Linear Regression Introduction Bayesian Inference for Linear Regression Prediction Model Selection and Checking Model Selection by DIC Posterior Predictive Model Checking Cross-validation Model Checking Bayesian Residual Analysis Robust Regression Analysis of Variance Ridge Regression for Multicollinearity Regression with Autoregressive Errors 4.Generalized Linear Models GLMs Binary Responses Count Responses Poisson Regression Negative binomial regression Modeling Rates Gamma Regression for Skewed Data Proportional Responses Modeling Zero-inflated Data 5.Linear Mixed and Generalized Linear Mixed Models Linear Mixed Models Single Random Effect Choice of Priors Random Effects Longitudinal Data Random Intercept Random Slope and Intercept Prediction Classical Z-matrix Model Ridge Regression Revisited Generalized Linear Mixed Models Poisson GLMM Binary GLMM Improving the Approximation 6.Survival Analysis Introduction Semiparametric Models Piecewise Constant Baseline Hazard Models Stratified Proportional Hazards Models Accelerated Failure Time Models Model Diagnosis Interval Censored Data Frailty Models Joint Modeling of Longitudinal and Time-to-event Data 7.Random Walk Models for Smoothing Methods Introduction Smoothing Splines Random Walk (RW) Priors for Equally-spaced Locations Choice of Priors on s e and sf Random Walk Models for Non-equally Spaced Locations Thin-plate Splines Thin-plate Splines on Regular Lattices Thin-plate Splines at Irregularly-spaced Locations Besag Spatial Model Penalized Regression Splines (P-splines) Adaptive Spline Smoothing Generalized Nonparametric Regression Models Excursion Set with Uncertainty 8.Gaussian Process Regression Introduction Penalized Complexity Priors Credible Bands for Smoothness Non-stationary Fields Interpolation with Uncertainty Survival Response 9.Additive and Generalized Additive Models Additive Models Generalized Additive Models Binary response Count response Generalized Additive Mixed Models 10.Errors-in-Variables Regression Introduction Classical Errors-in-Variables Models A simple linear model with heteroscedastic errors-invariables A general exposure model with replicated measurements Berkson Errors-in-Variables Models 11.Miscellaneous Topics in INLA Splines as a Mixed Model Truncated Power Basis Splines O'Sullivan Splines Example: Canadian Income Data Analysis of Variance for Functional Data Extreme Values Density Estimation using INLA Appendix A Installation Appendix B Uninformative Priors in Linear Regression Index
Series Title: Chapman & Hall/CRC computer science and data analysis series
Other Titles: Bayesian regression modeling with integrated Laplace approximation
Responsibility: Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway.

Abstract:

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.--

Reviews

Editorial reviews

Publisher Synopsis

"The book focuses on regression models with R-INLA and it will be of interest to a wide audience. INLA is becoming a very popular method for approximate Bayesian inference and it is being applied to Read more...

 
User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/1008765295> # Bayesian regression modeling with INLA
    a schema:CreativeWork, schema:Book ;
   library:oclcnum "1008765295" ;
   library:placeOfPublication <http://id.loc.gov/vocabulary/countries/flu> ;
   schema:about <http://experiment.worldcat.org/entity/work/data/4664314660#Topic/bayesian_statistical_decision_theory> ; # Bayesian statistical decision theory
   schema:about <http://experiment.worldcat.org/entity/work/data/4664314660#Topic/laplace_transformation> ; # Laplace transformation
   schema:about <http://experiment.worldcat.org/entity/work/data/4664314660#Topic/regression_analysis> ; # Regression analysis
   schema:about <http://dewey.info/class/519.542/e23/> ;
   schema:about <http://experiment.worldcat.org/entity/work/data/4664314660#Topic/gaussian_processes> ; # Gaussian processes
   schema:alternateName "Bayesian regression modeling with integrated Laplace approximation" ;
   schema:author <http://experiment.worldcat.org/entity/work/data/4664314660#Person/faraway_julian_james> ; # Julian James Faraway
   schema:author <http://experiment.worldcat.org/entity/work/data/4664314660#Person/yue_yu_1981> ; # Yu Yue
   schema:author <http://experiment.worldcat.org/entity/work/data/4664314660#Person/wang_xiaofeng_professor_of_medicine> ; # (Professor of medicine) Xiaofeng Wang
   schema:bookFormat bgn:PrintBook ;
   schema:datePublished "2018" ;
   schema:description "This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.--"@en ;
   schema:exampleOfWork <http://worldcat.org/entity/work/id/4664314660> ;
   schema:inLanguage "en" ;
   schema:name "Bayesian regression modeling with INLA"@en ;
   schema:productID "1008765295" ;
   schema:workExample <http://worldcat.org/isbn/9781498727259> ;
   wdrs:describedby <http://www.worldcat.org/title/-/oclc/1008765295> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/4664314660#Person/faraway_julian_james> # Julian James Faraway
    a schema:Person ;
   schema:familyName "Faraway" ;
   schema:givenName "Julian James" ;
   schema:name "Julian James Faraway" ;
    .

<http://experiment.worldcat.org/entity/work/data/4664314660#Person/wang_xiaofeng_professor_of_medicine> # (Professor of medicine) Xiaofeng Wang
    a schema:Person ;
   schema:familyName "Wang" ;
   schema:givenName "Xiaofeng" ;
   schema:name "(Professor of medicine) Xiaofeng Wang" ;
    .

<http://experiment.worldcat.org/entity/work/data/4664314660#Topic/bayesian_statistical_decision_theory> # Bayesian statistical decision theory
    a schema:Intangible ;
   schema:name "Bayesian statistical decision theory"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/4664314660#Topic/laplace_transformation> # Laplace transformation
    a schema:Intangible ;
   schema:name "Laplace transformation"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/4664314660#Topic/regression_analysis> # Regression analysis
    a schema:Intangible ;
   schema:name "Regression analysis"@en ;
    .

<http://worldcat.org/isbn/9781498727259>
    a schema:ProductModel ;
   schema:isbn "1498727255" ;
   schema:isbn "9781498727259" ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.