| Titre : |
Markov chain Monte Carlo in practice |
| Type de document : |
texte imprimé |
| Auteurs : |
Gilks, W. R., Éditeur scientifique ; Richardson, Sylvia, Éditeur scientifique ; Spiegelhalter, D. J., Éditeur scientifique |
| Editeur : |
Boca Raton [Etats-Unis] : Chapman & Hall / CRC |
| Année de publication : |
1996 |
| Importance : |
XVII, 486 p. |
| Présentation : |
ill. |
| Format : |
25 cm |
| ISBN/ISSN/EAN : |
978-0-412-05551-5 |
| Note générale : |
Première réimpression de CRC Press en 1998. - La couv. porte aussi : "Interdisciplinary statistics" . - Bibliogr. en fin de chapitre. - Index |
| Langues : |
Français (fre) |
| Mots-clés : |
Markov processes
Monte Carlo method
Medical statistics
Biometry
Marches aléatoires (mathématiques)
Biométrie
Statistique médicale
Markov, Processus de
Monte-Carlo, Méthode de |
| Index. décimale : |
519.217 Processus de Markov |
| Résumé : |
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation.Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains.Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well. |
| Note de contenu : |
Summary :
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. Mcmc maximum likelihood
25. Mixtures of distributions: inference and estimation
26. An archaeological example: radiocarbon dating |
Markov chain Monte Carlo in practice [texte imprimé] / Gilks, W. R., Éditeur scientifique ; Richardson, Sylvia, Éditeur scientifique ; Spiegelhalter, D. J., Éditeur scientifique . - Boca Raton [Etats-Unis] : Chapman & Hall / CRC, 1996 . - XVII, 486 p. : ill. ; 25 cm. ISBN : 978-0-412-05551-5 Première réimpression de CRC Press en 1998. - La couv. porte aussi : "Interdisciplinary statistics" . - Bibliogr. en fin de chapitre. - Index Langues : Français ( fre)
| Mots-clés : |
Markov processes
Monte Carlo method
Medical statistics
Biometry
Marches aléatoires (mathématiques)
Biométrie
Statistique médicale
Markov, Processus de
Monte-Carlo, Méthode de |
| Index. décimale : |
519.217 Processus de Markov |
| Résumé : |
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation.Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains.Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well. |
| Note de contenu : |
Summary :
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. Mcmc maximum likelihood
25. Mixtures of distributions: inference and estimation
26. An archaeological example: radiocarbon dating |
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