Bayesian data analysis:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
Chapman & Hall/CRC
2004
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Texts in statistical science series
[60] |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXV, 668 S. graph. Darst. |
| ISBN: | 158488388X 9781584883883 |
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Datensatz im Suchindex
| _version_ | 1804130018685616128 |
|---|---|
| adam_text | BAYESIAN DATA ANALYSIS SECOND EDITION ANDREW GELMAN COLUMBIA UNIVERSITY,
NEWYORK /JOHN B. CARLIN UNIVERSITY OF MELBOURNE, AUSTRALIA HAL S. STERN
UNIVERSITY OF CALIFORNIA, IRVINE DONALD B. RUBIN HARVARD UNIVERSITY,
CAMBRIDGE, MASSACHUSETTS CHAPMAN & HALL/CRC A CRC PRESS COMPANY BOCA
RATON LONDON NEWYORK WASHINGTON, D.C. CONTENTS LIST OF MODELS XV LIST OF
EXAMPLES XVII PREFACE XIX PART I: FUNDAMENTALS OF BAYESIAN INFERENCE 1 1
BACKGROUND 3 1 .1 OVERVIEW 3 1.2 GENERAL NOTATION FOR
STATISTICA^ INFERENCE 4 1.3 BAYESIAN INFERENCE 6 1.4 EXAMPLE: INFERENCE
ABOUT A GENETIC PROBABILITY 9 1.5 PROBABILITY AS A MEASURE OF
UNCERTAINTY 11 1.6 EXAMPLE OF PROBABILITY ASSIGNMENT: FOOTBALL POINT
SPREADS 14 1.7 EXAMPLE OF PROBABILITY ASSIGNMENT: ESTIMATING THE
ACCURACY OF RECORD LINKAGE 17 1.8 SOME USEFUL RESULTS FROM PROBABILITY
THEORY 22 1.9 SUMMARIZING INFERENCES BY SIMULATION 25 1.10 COMPUTATION
AND SOFTWARE 27 1.11 BIBLIOGRAPHIC NOTE 27 1.12 EXERCISES 29 2
SINGLE-PARAMETER MODELS 33 2.1 ESTIMATING A PROBABILITY FROM BINOMIAL
DATA 33 2.2 POSTERIOR DISTRIBUTION AS COMPROMISE BETWEEN DATA AND PRIOR
INFORMATION 36 2.3 SUMMARIZING POSTERIOR INFERENCE 37 2.4 INFORMATIVE
PRIOR DISTRIBUTIONS 39 2.5 EXAMPLE: ESTIMATING THE PROBABILITY OF A
FEMALE BIRTH GIVEN PLACENTA PREVIA 43 2.6 ESTIMATING THE MEAN OF A
NORMAL DISTRIBUTION WITH KNOWN VARIANCE 46 2.7 OTHER STANDARD
SINGLE-PARAMETER MODELS 49 2.8 EXAMPLE: INFORMATIVE PRIOR DISTRIBUTION
AND MULTILEVEL STRUC- TURE FOR ESTIMATING CANCER RATES 55 VIII CONTENTS
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 PROPER^*S 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 117 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 CONTENTS IX 6.5 NUMERICAL
POSTERIOR PREDICTIVE CHECKS 172 6.6 MODEL EXPANSION 177 6.7 MODEL
COMPARISON 179 6.8 MODEL CHECKING FOR THE EDUCATIONAL TESTING EXAMPLE
186 6.9 BIBLIOGRAPHIC NOTE 190 6.10 EXERCISES 192 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 . X CONTENTS
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 MODEL 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 CONTENTS XI 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 MODELING AS AN ALTERNATIVE TO
SELECTING 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-I 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 XII CONTENTS 19.4 MULTIVARIATE
MODELS FOR NONNORMAL DATA 488 19.5 TIME SERIES AND SPATIAL MODELS 491
19.6 BIBLIOGRAPHIC NOTE 493 19.7 EXERCISES 494 20 NONLINEAR MODELS 497
20.1 INTRODUCTION 497 20.2 EXAMPLE: SERIAL DILUTION ASSAY 498 20.3
EXAMPLE; POPULATION TOXICOKINETICS 504 20.4 BIBLIOGRAPHIC NOTE 514 20.5
EXERCISES 515 21 MODELS FOR MISSING DATA 517 21.1 NOTATION 517 21.2
MULTIPLE IMPUTATION 519 21.3 MISSING DATA IN THE MULTIVARIATE NORMAL AND
T MODELS 523 21.4 EXAMPLE: MULTIPLE IMPUTATION FOR A SERIES OF POLLS 526
21.5 MISSING VALUES WITH COUNTED DATA 533 21.6 EXAMPLE: AN OPINION POLL
IN SLOVENIA 534 21.7 BIBLIOGRAPHIC NOTE 539 21.8 EXERCISES 540 22
DECISION ANALYSIS 541 22.1 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.I 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.I BIBLIOGRAPHIC
NOTE 589 C EXAMPLE OF COMPUTATION IN R AND BUGS 591 C.I GETTING STARTED
WITH R AND BUGS 591 CONTENTS 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 592 596 600 607 608 611 647 655
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| callnumber-subject | QA - Mathematics |
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| ctrlnum | (OCoLC)51991499 (DE-599)BVBBV017154097 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| genre | 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV017154097 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T19:14:22Z |
| institution | BVB |
| isbn | 158488388X 9781584883883 |
| language | English |
| lccn | 2003051474 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-010341196 |
| oclc_num | 51991499 |
| open_access_boolean | |
| owner | DE-384 DE-N2 DE-91G DE-BY-TUM DE-824 DE-703 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-91 DE-BY-TUM DE-945 DE-M347 DE-521 DE-83 DE-706 DE-188 DE-11 DE-29 |
| owner_facet | DE-384 DE-N2 DE-91G DE-BY-TUM DE-824 DE-703 DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-91 DE-BY-TUM DE-945 DE-M347 DE-521 DE-83 DE-706 DE-188 DE-11 DE-29 |
| physical | XXV, 668 S. graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series | Texts in statistical science series |
| series2 | Texts in statistical science series |
| spelling | Bayesian data analysis Andrew Gelman ... 2. ed. Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2004 XXV, 668 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Texts in statistical science series [60] Análise de dados larpcal Besliskunde gtt Data-analyse gtt Inferência bayesiana (inferência estatística) larpcal Inferência paramétrica larpcal Methode van Bayes gtt Statistique bayésienne Teoria da decisão (inferência estatística) larpcal Bayesian statistical decision theory Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Bayes-Entscheidungstheorie (DE-588)4144220-9 s Bayes-Verfahren (DE-588)4204326-8 s Regressionsmodell (DE-588)4127980-3 s Nichtparametrisches Verfahren (DE-588)4339273-8 s 2\p DE-604 Gelman, Andrew 1965- Sonstige (DE-588)128832592 oth Texts in statistical science series [60] (DE-604)BV022819715 60 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010341196&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bayesian data analysis Texts in statistical science series Análise de dados larpcal Besliskunde gtt Data-analyse gtt Inferência bayesiana (inferência estatística) larpcal Inferência paramétrica larpcal Methode van Bayes gtt Statistique bayésienne Teoria da decisão (inferência estatística) larpcal Bayesian statistical decision theory Regressionsmodell (DE-588)4127980-3 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4127980-3 (DE-588)4339273-8 (DE-588)4204326-8 (DE-588)4144220-9 (DE-588)4143413-4 |
| title | Bayesian data analysis |
| title_auth | Bayesian data analysis |
| title_exact_search | Bayesian data analysis |
| title_full | Bayesian data analysis Andrew Gelman ... |
| title_fullStr | Bayesian data analysis Andrew Gelman ... |
| title_full_unstemmed | Bayesian data analysis Andrew Gelman ... |
| title_short | Bayesian data analysis |
| title_sort | bayesian data analysis |
| topic | Análise de dados larpcal Besliskunde gtt Data-analyse gtt Inferência bayesiana (inferência estatística) larpcal Inferência paramétrica larpcal Methode van Bayes gtt Statistique bayésienne Teoria da decisão (inferência estatística) larpcal Bayesian statistical decision theory Regressionsmodell (DE-588)4127980-3 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Análise de dados Besliskunde Data-analyse Inferência bayesiana (inferência estatística) Inferência paramétrica Methode van Bayes Statistique bayésienne Teoria da decisão (inferência estatística) Bayesian statistical decision theory Regressionsmodell Nichtparametrisches Verfahren Bayes-Verfahren Bayes-Entscheidungstheorie Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010341196&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022819715 |
| work_keys_str_mv | AT gelmanandrew bayesiandataanalysis |