| Titre : | Markov Chain Monte Carlo in Practice | | Type de document : | texte imprimé | | Auteurs : | W.R. Gilks, Auteur ; S. Richardson, Auteur ; D.J. Spiegelhalter, Auteur | | Editeur : | Boca Raton, London, New York : Chapman & hALL/CRC | | Année de publication : | 1996 | | Importance : | 486 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 23,7 cm. | | ISBN/ISSN/EAN : | 978-0-412-05551-5 | | Langues : | Anglais (eng) | | Index. décimale : | 25-02 Théorie et traitement du signal | | Résumé : | Markov Chain Monte Carlo (MCMC) methods are simulation-based methods which can be used for the analysis of complex statistical models. Although MCMC techniques have been used in statistical physics for many years. Their usefulness for general statistical modelling has only recently been appreciated.
The literature on MCMC methodology and its applications is scattered and rapidly expanding this book draws together contributions from authorities in the field and fills the urgent need to communicate the state of the art to a general statistical audience.
Emphasis is placed on practice rather than theory. although fundamental theoretical concepts are discussed. The issues covered arise in real application such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis.This introductory-level texts is suitable for applied statisticians, biostatisticians and statistically-oriented epidemiologists and computer scientists. | | Note de contenu : | Contents
1. INTRODUCING MARKOV CHAIN MONTE CARLO
2. HEPATITIS B: A CASE STUDY IN MCMC METHODS
3. MARKOV CHAIN CONCEPTS RELATED TO SAMPLING ALGORITHMS
4. INTRODUCTION TO GENERAL STATE-SPACE MARKOV CHAIN THEORY
5. FULL CONDITIONAL DISTRIBUTIONS
6. STRATEGIES FOR IMPROVING MCMC
7. IMPLEMENTING MCMC
8. INFERENCE AND MONITORING CONVERGENCE
9. MODEL DETERMINATION USING SAMPLING-BASED METHODS
10. HYPOTHESIS TESTING AND MODEL SELECTION
11. MODEL CHECKING AND MODEL IMPROVEMENT
12. STOCHASTIC SEARCH VARIABLE SELECTION
13. BAYESIAN MODEL COMPARISON VIA JUMP DIFFUSIONS
14. ESTIMATION AND OPTIMIZATION OF FUNCTIONS
15. STOCHASTIC EM: METHOD AND APPLICATION
16. GENERALIZED LINEAR MIXED MODELS
17. HIERARCHICAL LONGITUDINAL MODELLING
18. MEDICAL MONITORING
19. MCMC FOR NONLINEAR HIERARCHICAL MODELS
20. BAYESIAN MAPPING OF DISEASE
21. MCMC IN IMAGE ANALYSIS
22. MEASUREMENT ERROR
23. GIBBS SAMPLING METHODS IN GENETICS
24. MIXTURES OF DISTRIBUTIONS: INFERENCE AND ESTIMATION
25. AN ARCHAEOLOGICAL EXAMPLE: RADIOCARBON DATING
Index |
Markov Chain Monte Carlo in Practice [texte imprimé] / W.R. Gilks, Auteur ; S. Richardson, Auteur ; D.J. Spiegelhalter, Auteur . - Boca Raton, London, New York : Chapman & hALL/CRC, 1996 . - 486 p. : couv. ill. en coul., ill. ; 23,7 cm. ISBN : 978-0-412-05551-5 Langues : Anglais ( eng) | Index. décimale : | 25-02 Théorie et traitement du signal | | Résumé : | Markov Chain Monte Carlo (MCMC) methods are simulation-based methods which can be used for the analysis of complex statistical models. Although MCMC techniques have been used in statistical physics for many years. Their usefulness for general statistical modelling has only recently been appreciated.
The literature on MCMC methodology and its applications is scattered and rapidly expanding this book draws together contributions from authorities in the field and fills the urgent need to communicate the state of the art to a general statistical audience.
Emphasis is placed on practice rather than theory. although fundamental theoretical concepts are discussed. The issues covered arise in real application such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis.This introductory-level texts is suitable for applied statisticians, biostatisticians and statistically-oriented epidemiologists and computer scientists. | | Note de contenu : | Contents
1. INTRODUCING MARKOV CHAIN MONTE CARLO
2. HEPATITIS B: A CASE STUDY IN MCMC METHODS
3. MARKOV CHAIN CONCEPTS RELATED TO SAMPLING ALGORITHMS
4. INTRODUCTION TO GENERAL STATE-SPACE MARKOV CHAIN THEORY
5. FULL CONDITIONAL DISTRIBUTIONS
6. STRATEGIES FOR IMPROVING MCMC
7. IMPLEMENTING MCMC
8. INFERENCE AND MONITORING CONVERGENCE
9. MODEL DETERMINATION USING SAMPLING-BASED METHODS
10. HYPOTHESIS TESTING AND MODEL SELECTION
11. MODEL CHECKING AND MODEL IMPROVEMENT
12. STOCHASTIC SEARCH VARIABLE SELECTION
13. BAYESIAN MODEL COMPARISON VIA JUMP DIFFUSIONS
14. ESTIMATION AND OPTIMIZATION OF FUNCTIONS
15. STOCHASTIC EM: METHOD AND APPLICATION
16. GENERALIZED LINEAR MIXED MODELS
17. HIERARCHICAL LONGITUDINAL MODELLING
18. MEDICAL MONITORING
19. MCMC FOR NONLINEAR HIERARCHICAL MODELS
20. BAYESIAN MAPPING OF DISEASE
21. MCMC IN IMAGE ANALYSIS
22. MEASUREMENT ERROR
23. GIBBS SAMPLING METHODS IN GENETICS
24. MIXTURES OF DISTRIBUTIONS: INFERENCE AND ESTIMATION
25. AN ARCHAEOLOGICAL EXAMPLE: RADIOCARBON DATING
Index |
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