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Bayesian Data Analysis 3rd edition [Kietas viršelis]

(Department of Statistics, Columbia University, New York, USA), (Aalto University), (Duke University, Durham, North Carolina, USA), , (The Royal Children's Hospital, Parkville, Victoria, Australia), (University of California, Irvine, USA)
  • Formatas: Hardback, 675 pages, aukštis x plotis: 254x178 mm, weight: 1324 g, 49 Tables, black and white; 121 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 01-Nov-2013
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1439840954
  • ISBN-13: 9781439840955
Kitos knygos pagal šią temą:
Bayesian Data Analysis 3rd editionDidesnis paveikslėlis
  • Formatas: Hardback, 675 pages, aukštis x plotis: 254x178 mm, weight: 1324 g, 49 Tables, black and white; 121 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 01-Nov-2013
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1439840954
  • ISBN-13: 9781439840955
Kitos knygos pagal šią temą:
Kitos knygos iš šio išskirtinio pasiūlymo: Taylor & Francis siūlo daug technologijos knygų ISE (International Student Edition) kainomis
Pastovi nuoroda: https://www.kriso.lt/db/9781439840955.html
Raktažodžiai:
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

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 authorsall leaders in the statistics communityintroduce 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 Edition











Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code

The 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 books web page.

Recenzijos

"The second edition was reviewed in JASA by Maiti (2004) we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. this being a third edition begets the question of what is new when compared with the second edition? Quite a lot this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis." Christian P. Robert, Journal of the American Statistical Association, September 2014, Vol. 109

Praise for the Second Edition: it is simply the best all-around modern book focused on data analysis currently available. There is enough important additional material here that those with the first edition should seriously consider updating to the new version. when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice. Lawrence Joseph, Montreal General Hospital and McGill University, Statistics in Medicine, Vol. 23, 2004

I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems. John Grego, University of South Carolina, USA

easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods David Blackwell, University of California, Berkeley, USA "The second edition was reviewed in JASA by Maiti (2004) we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. this being a third edition begets the question of what is new when compared with the second edition? Quite a lot this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis." Christian P. Robert, Journal of the American Statistical Association, September 2014, Vol. 109

Praise for the Second Edition it is simply the best all-around modern book focused on data analysis currently available. There is enough important additional material here that those with the first edition should seriously consider updating to the new version. when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice. Lawrence Joseph, Montreal General Hospital and McGill University, Statistics in Medicine, Vol. 23, 2004

I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems. John Grego, University of South Carolina, USA

easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods David Blackwell, University of California, Berkeley, USA

Preface xiii
Part I: Fundamentals of Bayesian Inference 1(138)
1 Probability and inference
3(26)
1.1 The three steps of Bayesian data analysis
3(1)
1.2 General notation for statistical inference
4(2)
1.3 Bayesian inference
6(2)
1.4 Discrete probability examples: genetics and spell checking
8(3)
1.5 Probability as a measure of uncertainty
11(2)
1.6 Example of probability assignment: football point spreads
13(3)
1.7 Example: estimating the accuracy of record linkage
16(3)
1.8 Some useful results from probability theory
19(3)
1.9 Computation and software
22(2)
1.10 Bayesian inference in applied statistics
24(1)
1.11 Bibliographic note
25(2)
1.12 Exercises
27(2)
2 Single-parameter models
29(34)
2.1 Estimating a probability from binomial data
29(3)
2.2 Posterior as compromise between data and prior information
32(1)
2.3 Summarizing posterior inference
32(2)
2.4 Informative prior distributions
34(5)
2.5 Estimating a normal mean with known variance
39(3)
2.6 Other standard single-parameter models
42(5)
2.7 Example: informative prior distribution for cancer rates
47(4)
2.8 Noninformative prior distributions
51(4)
2.9 Weakly informative prior distributions
55(1)
2.10 Bibliographic note
56(1)
2.11 Exercises
57(6)
3 Introduction to multiparameter models
63(20)
3.1 Averaging over 'nuisance parameters'
63(1)
3.2 Normal data with a noninformative prior distribution
64(3)
3.3 Normal data with a conjugate prior distribution
67(2)
3.4 Multinomial model for categorical data
69(1)
3.5 Multivariate normal model with known variance
70(2)
3.6 Multivariate normal with unknown mean and variance
72(2)
3.7 Example: analysis of a bioassay experiment
74(4)
3.8 Summary of elementary modeling and computation
78(1)
3.9 Bibliographic note
78(1)
3.10 Exercises
79(4)
4 Asymptotics and connections to non-Bayesian approaches
83(18)
4.1 Normal approximations to the posterior distribution
83(4)
4.2 Large-sample theory
87(2)
4.3 Counterexamples to the theorems
89(2)
4.4 Frequency evaluations of Bayesian inferences
91(1)
4.5 Bayesian interpretations of other statistical methods
92(5)
4.6 Bibliographic note
97(1)
4.7 Exercises
98(3)
5 Hierarchical models
101(38)
5.1 Constructing a parameterized prior distribution
102(2)
5.2 Exchangeability and setting up hierarchical models
104(4)
5.3 Fully Bayesian analysis of conjugate hierarchical models
108(5)
5.4 Estimating exchangeable parameters from a normal model
113(6)
5.5 Example: parallel experiments in eight schools
119(5)
5.6 Hierarchical modeling applied to a meta-analysis
124(4)
5.7 Weakly informative priors for hierarchical variance parameters
128(4)
5.8 Bibliographic note
132(2)
5.9 Exercises
134(5)
Part II: Fundamentals of Bayesian Data Analysis 139(120)
6 Model checking
141(24)
6.1 The place of model checking in applied Bayesian statistics
141(1)
6.2 Do the inferences from the model make sense?
142(1)
6.3 Posterior predictive checking
143(10)
6.4 Graphical posterior predictive checks
153(6)
6.5 Model checking for the educational testing example
159(2)
6.6 Bibliographic note
161(2)
6.7 Exercises
163(2)
7 Evaluating, comparing, and expanding models
165(32)
7.1 Measures of predictive accuracy
166(3)
7.2 Information criteria and cross-validation
169(9)
7.3 Model comparison based on predictive performance
178(4)
7.4 Model comparison using Bayes factors
182(2)
7.5 Continuous model expansion
184(3)
7.6 Implicit assumptions and model expansion: an example
187(5)
7.7 Bibliographic note
192(1)
7.8 Exercises
193(4)
8 Modeling accounting for data collection
197(40)
8.1 Bayesian inference requires a model for data collection
197(2)
8.2 Data-collection models and ignorability
199(6)
8.3 Sample surveys
205(9)
8.4 Designed experiments
214(4)
8.5 Sensitivity and the role of randomization
218(2)
8.6 Observational studies
220(4)
8.7 Censoring and truncation
224(5)
8.8 Discussion
229(1)
8.9 Bibliographic note
229(1)
8.10 Exercises
230(7)
9 Decision analysis
237(22)
9.1 Bayesian decision theory in different contexts
237(2)
9.2 Using regression predictions: incentives for telephone surveys
239(6)
9.3 Multistage decision making: medical screening
245(1)
9.4 Hierarchical decision analysis for radon measurement
246(10)
9.5 Personal vs. institutional decision analysis
256(1)
9.6 Bibliographic note
257(1)
9.7 Exercises
257(2)
Part III: Advanced Computation 259(92)
10 Introduction to Bayesian computation
261(14)
10.1 Numerical integration
261(1)
10.2 Distributional approximations
262(1)
10.3 Direct simulation and rejection sampling
263(2)
10.4 Importance sampling
265(2)
10.5 How many simulation draws are needed?
267(1)
10.6 Computing environments
268(2)
10.7 Debugging Bayesian computing
270(1)
10.8 Bibliographic note
271(1)
10.9 Exercises
272(3)
11 Basics of Markov chain simulation
275(18)
11.1 Gibbs sampler
276(2)
11.2 Metropolis and Metropolis-Hastings algorithms
278(2)
11.3 Using Gibbs and Metropolis as building blocks
280(1)
11.4 Inference and assessing convergence
281(5)
11.5 Effective number of simulation draws
286(2)
11.6 Example: hierarchical normal model
288(3)
11.7 Bibliographic note
291(1)
11.8 Exercises
291(2)
12 Computationally efficient Markov chain simulation
293(18)
12.1 Efficient Gibbs samplers
293(2)
12.2 Efficient Metropolis jumping rules
295(2)
12.3 Further extensions to Gibbs and Metropolis
297(3)
12.4 Hamiltonian Monte Carlo
300(5)
12.5 Hamiltonian dynamics for a simple hierarchical model
305(2)
12.6 Stan: developing a computing environment
307(1)
12.7 Bibliographic note
308(1)
12.8 Exercises
309(2)
13 Modal and distributional approximations
311(40)
13.1 Finding posterior modes
311(2)
13.2 Boundary-avoiding priors for modal summaries
313(5)
13.3 Normal and related mixture approximations
318(2)
13.4 Finding marginal posterior modes using EM
320(5)
13.5 Approximating conditional and marginal posterior densities
325(1)
13.6 Example: hierarchical normal model (continued)
326(5)
13.7 Variational inference
331(7)
13.8 Expectation propagation
338(5)
13.9 Other approximations
343(2)
13.10 Unknown normalizing factors
345(3)
13.11 Bibliographic note
348(1)
13.12 Exercises
349(2)
Part IV: Regression Models 351(118)
14 Introduction to regression models
353(28)
14.1 Conditional modeling
353(1)
14.2 Bayesian analysis of the classical regression model
354(4)
14.3 Regression for causal inference: incumbency in congressional elections
358(6)
14.4 Goals of regression analysis
364(1)
14.5 Assembling the matrix of explanatory variables
365(2)
14.6 Regularization and dimension reduction for multiple predictors
367(2)
14.7 Unequal variances and correlations
369(7)
14.8 Including numerical prior information
376(2)
14.9 Bibliographic note
378(1)
14.10 Exercises
378(3)
15 Hierarchical linear models
381(24)
15.1 Regression coefficients exchangeable in batches
382(1)
15.2 Example: forecasting U.S. presidential elections
383(5)
15.3 Interpreting a normal prior distribution as additional data
388(2)
15.4 Varying intercepts and slopes
390(2)
15.5 Computation: batching and transformation
392(3)
15.6 Analysis of variance and the batching of coefficients
395(3)
15.7 Hierarchical models for batches of variance components
398(2)
15.8 Bibliographic note
400(2)
15.9 Exercises
402(3)
16 Generalized linear models
405(30)
16.1 Standard generalized linear model likelihoods
406(1)
16.2 Working with generalized linear models
407(5)
16.3 Weakly informative priors for logistic regression
412(8)
16.4 Example: hierarchical Poisson regression for police stops
420(2)
16.5 Example: hierarchical logistic regression for political opinions
422(1)
16.6 Models for multivariate and multinomial responses
423(5)
16.7 Loglinear models for multivariate discrete data
428(3)
16.8 Bibliographic note
431(1)
16.9 Exercises
432(3)
17 Models for robust inference
435(14)
17.1 Aspects of robustness
435(2)
17.2 Overdispersed versions of standard probability models
437(2)
17.3 Posterior inference and computation
439(2)
17.4 Robust inference and sensitivity analysis for the eight schools
441(3)
17.5 Robust regression using t-distributed errors
444(1)
17.6 Bibliographic note
445(1)
17.7 Exercises
446(3)
18 Models for missing data
449(20)
18.1 Notation
449(2)
18.2 Multiple imputation
451(3)
18.3 Missing data in the multivariate normal and t models
454(2)
18.4 Example: multiple imputation for a series of polls
456(6)
18.5 Missing values with counted data
462(1)
18.6 Example: an opinion poll in Slovenia
463(3)
18.7 Bibliographic note
466(1)
18.8 Exercises
467(2)
Part V: Nonlinear and Nonparametric Models 469(106)
19 Parametric nonlinear models
471(16)
19.1 Example: serial dilution assay
471(6)
19.2 Example: population toxicokinetics
477(8)
19.3 Bibliographic note
485(1)
19.4 Exercises
486(1)
20 Basis function models
487(14)
20.1 Splines and weighted sums of basis functions
487(3)
20.2 Basis selection and shrinkage of coefficients
490(4)
20.3 Non-normal models and multivariate regression surfaces
494(4)
20.4 Bibliographic note
498(1)
20.5 Exercises
498(3)
21 Gaussian process models
501(18)
21.1 Gaussian process regression
501(4)
21.2 Example: birthdays and birthdates
505(5)
21.3 Latent Gaussian process models
510(2)
21.4 Functional data analysis
512(1)
21.5 Density estimation and regression
513(3)
21.6 Bibliographic note
516(1)
21.7 Exercises
516(3)
22 Finite mixture models
519(26)
22.1 Setting up and interpreting mixture models
519(5)
22.2 Example: reaction times and schizophrenia
524(9)
22.3 Label switching and posterior computation
533(3)
22.4 Unspecified number of mixture components
536(3)
22.5 Mixture models for classification and regression
539(3)
22.6 Bibliographic note
542(1)
22.7 Exercises
543(2)
23 Dirichlet process models
545(30)
23.1 Bayesian histograms
545(1)
23.2 Dirichlet process prior distributions
546(3)
23.3 Dirichlet process mixtures
549(8)
23.4 Beyond density estimation
557(3)
23.5 Hierarchical dependence
560(8)
23.6 Density regression
568(3)
23.7 Bibliographic note
571(2)
23.8 Exercises
573(2)
A Standard probability distributions 575(10)
A.1 Continuous distributions
575(8)
A.2 Discrete distributions
583(1)
A.3 Bibliographic note
584(1)
B Outline of proofs of limit theorems 585(4)
B.1 Bibliographic note
588(1)
C Computation in R and Stan 589(18)
C.1 Getting started with R and Stan
589(1)
C.2 Fitting a hierarchical model in Stan
589(5)
C.3 Direct simulation, Gibbs, and Metropolis in R
594(7)
C.4 Programming Hamiltonian Monte Carlo in R
601(4)
C.5 Further comments on computation
605(1)
C.6 Bibliographic note
606(1)
References 607(34)
Author Index 641(8)
Subject Index 649
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin