Bayesian methods: an analysis for statisticians and interdisciplinary researchers
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
1999
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 333 S. graph. Darst. |
| ISBN: | 0521594170 |
Internformat
MARC
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| 245 | 1 | 0 | |a Bayesian methods |b an analysis for statisticians and interdisciplinary researchers |c Thomas Leonard ; John S. J. Hsu |
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| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 1999 | |
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| 650 | 7 | |a Methode van Bayes |2 gtt | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface page xi
1 Introductory Statistical Concepts 1
1.0 Preliminaries and Overview 1
1.1 Sampling Models and Likelihoods 6
1.2 Practical Examples 19
1.3 Large Sample Properties of Likelihood Procedures 33
1.4 Practical Examples 42
1.5 S ome Further Properties of Likelihood 45
1.6 Practical Examples 63
1.7 The Midcontinental Rift 66
1.8 A Model for Genetic Traits in Dairy Science 68
1.9 Least Squares Regression with Serially Correlated Errors 68
1.10 Annual World Crude Oil Production (1880-1972) 69
2 The Discrete Version of Bayes’ Theorem 75
2.0 Preliminaries and Overview 75
2.1 Bayes’Theorem 76
2.2 Estimating a Discrete-Valued Parameter 81
2.3 Applications to Model Selection 82
2.4 Practical Examples 86
2.5 Logistic Discrimination and the Construction of Neural Nets 88
2.6 Anderson’s Prediction of Psychotic Patients 91
2.7 The Ontario Fetal Metabolic Acidosis Study 92
2.8 Practical Guidelines 96
3 Models with a Single Unknown Parameter 98
3.0 Preliminaries and Overview 98
3.1 The B ayesian Paradigm 99
3.2 Posterior and Predictive Inferences 105
3.3 Practical Examples 117
3.4 Inferences for a Normal Mean with Known Variance 120
3.5 Practical Examples 130
3.6 Vague Prior Information 134
3.7 Practical Examples 142
IX
X
Contents
3.8 Bayes Estimators and Decision Rules and Their
Frequency Properties 143
3.9 Practical Examples 155
3.10 Symmetric Loss Functions 157
3.11 Practical Example: Mixtures of Normal Distributions 163
4 The Expected Utility Hypothesis 165
4.0 Preliminaries and Overview 165
4.1 Classical Theory 166
4.2 The Savage Axioms 172
4.3 Modifications to the Expected Utility Hypothesis 176
4.4 The Experimental Measurement of e-Adjusted Utility 179
4.5 The Risk-Aversion Paradox 182
4.6 The Ellsberg Paradox 185
4.7 A Practical Case Study 187
5 Models with Several Unknown Parameters 189
5.0 Preliminaries and Overview 189
5.1 Bayesian Marginalization 190
5.2 Further Methods and Practical Examples 217
5.3 The Kalman Filter 233
5.4 An On-Line Analysis of Chemical Process Readings 237
5.5 An Industrial Control Chart 238
5.6 Forecasting Geographical Proportions for World Sales of Fibers 239
5.7 Bayesian Forecasting in Economics 240
6 Prior Structures, Posterior Smoothing, and
Bayes-Stein Estimation 242
6.0 Preliminaries and Overview 242
6.1 Multivariate Normal Priors for the Transformed Parameters 243
6.2 Posterior Mode Vectors and Laplacian Approximations 253
6.3 Prior Structures, and Modeling for Nonrandomized Data 259
6.4 Monte Carlo Methods and Importance Sampling 275
6.5 Further Special Cases and Practical Examples 281
6.6 Markov Chain Monte Carlo (MCMC) Methods:
The Gibbs Sampler 295
6.7 Modeling Sampling Distributions, Using MCMC 295
6.8 Equally Weighted Mixtures and Survivor Functions 297
6.9 A Hierarchical Bayes Analysis 300
References 303
Author Index 321
Subject Index
326
|
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| id | DE-604.BV012628149 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:30:54Z |
| institution | BVB |
| isbn | 0521594170 |
| language | English |
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| spelling | Leonard, Thomas Verfasser aut Bayesian methods an analysis for statisticians and interdisciplinary researchers Thomas Leonard ; John S. J. Hsu 1. publ. Cambridge [u.a.] Cambridge Univ. Press 1999 XIV, 333 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Cambridge series in statistical and probabilistic mathematics Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 s DE-604 Hsu, John S. J. Verfasser aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008578893&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Leonard, Thomas Hsu, John S. J. Bayesian methods an analysis for statisticians and interdisciplinary researchers Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4204326-8 |
| title | Bayesian methods an analysis for statisticians and interdisciplinary researchers |
| title_auth | Bayesian methods an analysis for statisticians and interdisciplinary researchers |
| title_exact_search | Bayesian methods an analysis for statisticians and interdisciplinary researchers |
| title_full | Bayesian methods an analysis for statisticians and interdisciplinary researchers Thomas Leonard ; John S. J. Hsu |
| title_fullStr | Bayesian methods an analysis for statisticians and interdisciplinary researchers Thomas Leonard ; John S. J. Hsu |
| title_full_unstemmed | Bayesian methods an analysis for statisticians and interdisciplinary researchers Thomas Leonard ; John S. J. Hsu |
| title_short | Bayesian methods |
| title_sort | bayesian methods an analysis for statisticians and interdisciplinary researchers |
| title_sub | an analysis for statisticians and interdisciplinary researchers |
| topic | Besliskunde gtt Methode van Bayes gtt Statistique bayésienne Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Besliskunde Methode van Bayes Statistique bayésienne Bayesian statistical decision theory Bayes-Verfahren |
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