TY  - GEN
AU  - Gelman, Andrew
AU  - Carlin, John B.
AU  - Stern, Hal Steven.
AU  - Dunson, David B.
AU  - Vehtari, Aki
AU  - Rubin, Donald B.
TI  - Bayesian data analysis
SN  - 9781439840955
U1  - 519.542 
PY  - 2019///
CY  - Boca Raton : 
PB  - CRC Press, 
KW  - Bayesian statistical decision theory
KW  - Bayesian probability
KW  - Bayesian linear regression
KW  - Bayesian inference
KW  - Bayesian data analysia
N1  - Contents:

Part I: Fundamentals of Bayesian inference. Probability and inference
Single-parameter models
Introduction to multiparameter models
Asymptotics and connections to non-Bayesian approaches
Hierarchical models
Part II: Fundamentals of Bayesian data analysis. Model checking
Evaluating, comparing, and expanding models
Modeling accounting for data collection
Decision analysis
Part III: Advanced computation. Introduction to Bayesian computation
Basics of Markov chain simulation
Computationally efficient Markov chain simulation
Modal and distributional approximations
Part IV: Regression models. Introduction to regression models
Hierarchical linear models
Generalized linear models
Models for robust inference
Models for missing data
Part V: Nonlinear and nonparametric models. Parametric nonlinear models
Basis function models
Gaussian process models
Finite mixture models
Dirichlet process models
A. Standard probability distributions
B. Outline of proofs of limit theorems
Computation in R and Stan
N2  - Summary:

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.New to the Third EditionFour new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page
ER  -