000			03542 a2200313 4500
005			20240612104113.0
008			240612b ||||| |||| 00| 0 eng d
020			_a9781439840955
082			_a519.542 _bGEL
100			_aGelman, Andrew _936256
245			_aBayesian data analysis
250			_a3rd ed.
260			_bCRC Press, _c2019 _aBoca Raton :
300			_axiv, 667 p. : _c26 cm.
440			_aTexts in statistical science _936335
500			_aContents: 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.
520			_aSummary: 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.
650			_aBayesian statistical decision theory _936336
650			_aBayesian probability _936337
650			_aBayesian linear regression _936338
650			_aBayesian inference _936339
650			_aBayesian data analysia _936340
700			_aCarlin, John B. _936341
700			_aStern, Hal Steven. _936342
700			_aDunson, David B. _936343
700			_aVehtari, Aki _936344
700			_aRubin, Donald B. _936345
942			_cBK
999			_c187326 _d187326