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【书名】Bayesian Data Analysis
【作者】Andrew Gelman, John B. Carlin, Hal S. Stern,and Donald B. Rubin􀀀
【出版社】Chapman & Hall/CRC 
【版本】SECOND EDITION
【出版日期】2004 年7月
【文件格式】DJVU
【文件大小】3.65M
【页数】896
【ISBN出版号】1-58488-388-X
【资料类别】如计量经济学
【市面定价】人民币956.00元
【扫描版还是影印版】扫描版
【是否缺页】完整
【关键词】Bayesian Data Analysis

【内容简介】The book demonstrates clearly the importance of Bayesian statistical thinking to the design,
model building, inference, and model checking involved in any scientific investigation...excellent
taste in both breadth and depth of topics covered, and the writing style is extremely lucid.

【目录】
        Contents

List of models

List of examples

Preface

Part I: Fundamentals of Bayesian Inference

1 Background
1.1 Overview
1.2 General notation for statistical inference
1.3 Bayesian inference
1.4 Example: inference about a genetic probability
1.5 Probability as a measure of uncertainty
1.6 Example of probability assignment: football point spreads
1.7 Example of probability assignment: estimating the accuracy of record linkage
1.8 Some useful results from probability theory
1.9 Summarizing inferences by simulation
1.10 Computation and software
1.11 Bibliographic note
1.12 Exercises

2 Single-parameter models
2.1 Estimating a probability from binomial data
2.2 Posterior distribution as compromise between data and prior
information
2.3 Summarizing posterior inference
2.4 Informative prior distributions
2.5 Example: estimating the probability of a female birth given placenta previa
2.6 Estimating the mean of a normal distribution with known varIance
2.7 Other standard single-parameter models
2.8 Example: informative prior distribution and multilevel structure for estimating cancer rates
2.9 Noninformative prior distributions 61
2.10 Bibliographic note 65
2.11 Exercises 67

3 Introduction to multiparameter models 73
3.1 Averaging over 'nuisance parameters' 73
3.2 Normal data with a noninformative prior distribution 74
3.3 Normal data with a conjugate prior distribution 78
3.4 Normal data with a semi-conjugate prior distribution 80
3.5 The multinomial model 83
3.6 The multivariate normal model 85
3.7 Example: analysis of a bioassay experiment 88
3.8 Summary of elementary modeling and computation 93
3.9 Bibliographic note 94
3.10 Exercises 95

4 Large-sample inference and frequency properties of Bayesian inference 101
4.1 Normal approximations to the posterior distribution 101
4.2 Large-sample theory 106
4.3 Counterexamples to the theorems 108
4.4 Frequency evaluations of Bayesian inferences 111
4.5 Bibliographic note 113
4.6 Exercises 113

Part II: Fundamentals of Bayesian Data Analysis 115

5 Hierarchical models 11 7
5.1 Constructing a parameterized prior distribution 118
5.2 Exchangeability and setting up hierarchical models 121
5.3 Computation with hierarchical models 125
5.4 Estimating an exchangeable set of parameters from a normal model 131
5.5 Example: combining information from educational testing
experiments in eight schools 138
5.6 Hierarchical modeling applied to a meta-analysis 145
5.7 Bibliographic note 150
5.8 Exercises 152

6 Model checking and improvement 157
6.1 The place of model checking in applied Bayesian statistics 157
6.2 Do the inferences from the model make sense? 158
6.3 Is the model consistent with data? Posterior predictive
checking 159
6.4 Graphical posterior predictive checks 165
6.5 Numerical posterior predictive checks
6.6 Model expansion
6.7 Model comparison
6.8 Model checking for the educational testing example
6.9 Bibliographic note
6.10 Exercises

7 Modeling accounting for data collection 197
7 .1 Introduction 197
7.2 Formal models for data collection 200
7.3 Ignorability 203
7.4 Sample surveys 207
7.5 Designed experiments 218
7.6 Sensitivity and the role of randomization 223
7.7 Observational studies 226
7.8 Censoring and truncation 231
7.9 Discussion 236
7.10 Bibliographic note 237
7.11 Exercises 239

8 Connections and challenges 247
8.1 Bayesian interpretations of other statistical methods 247
8.2 Challenges in Bayesian data analysis 252
8.3 Bibliographic note 255
8.4 Exercises 255

9 General advice 259
9.1 Setting up probability models 259
9.2 Posterior inference 264
9.3 Model evaluation 265
9.4 Summary 271
9.5 Bibliographic note 271

Part III: Advanced Computation 273

10 Overview of computation 275
10.1 Crude estimation by ignoring some information 276
10.2 Use of posterior simulations in Bayesian data analysis 276
10.3 Practical issues 278
10.4 Exercises 282

11 Posterior simulation 283
11.1 Direct simulation 283
11.2 Markov chain simulation 285
11.3 The Gibbs sampler 287
11.4 The Metropolis and Metropolis-Hastings algorithms 289
11.5 Building Markov chain algorithms using the Gibbs sampler
and Metropolis algorithm 292
11.6 Inference and assessing convergence 294
11.7 Example: the hierarchical normal model 299
11.8 Efficient Gibbs samplers 302
11.9 Efficient Metropolis jumping rules 305
11.10 Recommended strategy for posterior simulation 307
11.11 Bibliographic note 308
11.12 Exercises 310

12 Approximations based on posterior modes 311
12.1 Finding posterior modes 312
12.2 The normal and related mixture approximations 314
12.3 Finding marginal posterior modes using EM and related
algorithms 317
12.4 Approximating conditional and marginal posterior densities 324
12.5 Example: the hierarchical normal model (continued) 325
12.6 Bibliographic note 331
12.7 Exercises 332

13 Special topics in computation 335
13.1 Advanced techniques for Markov chain simulation 335
13.2 Numerical integration 340
13.3 Importance sampling 342
13.4 Computing normalizing factors 345
13.5 Bibliographic note 348
13.6 Exercises 349

Part IV: Regression Models 351

14 Introduction to regression models 353
14.1 Introduction and notation 353
14.2 Bayesian analysis of the classical regression mode] 355
14.3 Example: estimating the advantage of incumbency in U.S.
Congressional elections 359
14.4 Goals of regression analysis 367
14.5 Assembling the matrix of explanatory variables 369
14.6 Unequal variances and correlations 372
14.7 Models for unequal variances 375
14.8 Including prior information 382
14.9 Bibliographic note 385
14.10 Exercises 385
15 Hierarchical linear models 389
15.1 Regression coefficients exchangeable in batches 390
15.2 Example: forecasting U.S. Presidential elections 392
15.3 General notation for hierarchical linear models 399
15.4 Computation 400
15.5 Hierarchical rnodeling as an alternative to
electing predictors 405
15.6 Analysis of variance 406
15.7 Bibliographic note 411
15.8 Exercises 412

16 Generalized linear models 415
16.1 Introduction 415
16.2 Standard generalized linear model likelihoods 416
16.3 Setting up and interpreting generalized linear models 418
16.4 Computation 421
16.5 Example: hierarchical Poisson regression for police stops 425
16.6 Example: hierarchical logistic regression for political opinions 428
16.7 Models for multinomial responses 430
16.8 Loglinear models for multivariate discrete data 433
16.9 Bibliographic note 439
16.10 Exercises 440

17 Models for robust inference 443
17.1 Introduction 443
17.2 Overdispersed versions of standard probability models 445
17.3 Posterior inference and computation 448
17.4 Robust inference and sensitivity analysis for the educational
testing example 451
17.5 Robust regression using Student-t errors 455
17.6 Bibliographic note 457
17. 7 Exercises 458

Part V: Specific Models and Problems 461

18 Mixture models 463
18 .1 Introduction 463
18.2 Setting up mixture models 463
18.3 Computation 467
18.4 Example: reaction times and schizophrenia 468
18.5 Bibliographic note 479

19 Multivariate models 481
19.1 Linear regression with multiple outcomes 481
19.2 Prior distributions for covariance matrices 483
19.3 Hierarchical multivariate models 486
19.4 Multivariate models for nonnormal data
19.5 Time series and spatial models
19.6 Bibliographic note
19. 7 Exercises

20 Nonlinear models
20.1 Introduction
20.2 Example: serial dilution assay
20.3 Example: population toxicokinetics
20.4 Bibliographic note
20.5 Exercises

21 Models for missing data
21.1 Notation
21.2 Multiple imputation
21.3 Missing data in the multivariate normal and t models
21.4 Example: multiple imputation for a series of polls
21.5 Missing values with counted data
21.6 Example: an opinion poll in Slovenia
21.7 Bibliographic note
21.8 Exercises

22 Decision analysis 541
22.] Bayesian decision theory in different contexts 542
22.2 Using regression predictions: incentives for telephone surveys 544
22.3 Multistage decision making: medical screening 552
22.4 Decision analysis using a hierarchical model: home radon
measurement and remediation 555
22.5 Personal vs. institutional decision analysis 567
22.6 Bibliographic note 568
22.7 Exercises 569

Appendixes 571

A Standard probability distributions 573
A.1 Introduction 573
A.2 Continuous distributions 573
A.3 Discrete distributions .582
A.4 Bibliographic note 584

B Outline of proofs of asymptotic theorems 585
B.1 Bibliographic note 589

C Example of computation in R and Bugs 591
C.1 Getting started with R and Bugs 59]

C.2 Fitting a hierarchical model in Bugs
C.3 Options in the Bugs implementation
C.4 Fitting a hierarchical model in R
C.5 Further comments on computation
C.6 Bibliographic note

References

Author index

Subject index

【整理书评】
The second edition of Bayesian Data Analysis continues to emphasize practice over theory,
clearly describing how to conceptualize, perform, and critique statistical analyses from a
Bayesian perspective. Its world-class authors provide detailed guidance on all aspects of
Bayesian data analysis and include many examples of real statistical analyses, based on their
own research.