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008 190418b 2018 ||||| |||| 00| 0 eng d
020 _a9781498727259
082 _a519.542
_bW2B2
100 _aWang, Xiaofeng
_9378580
245 _aBayesian regression modeling with INLA
260 _bCRC Press
_c2018
_aFlorida
300 _axii, 312p.
_bWith index
440 _aComputer science and data analysis series
_9378587
504 _aTable 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 _aINLA 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 _aRegression analysis
_9378581
650 _aBayesian statistical decision theory
_9378582
650 _aGaussian processes
_9378583
650 _aLaplace transformation
_9378584
700 _aYue, Yu Ryan
_eCo author
_9378585
700 _aFaraway, Julian J.
_eCo author
_9378586
942 _2ddc
_cBK