| Titre : | Markov chain monte carlo : stochastic simulation for Bayesian inference |
| Auteurs : | Gamerman, Dani., Auteur ; Lopes, hedibert freitas, Auteur |
| Type de document : | texte imprimé |
| Mention d'édition : | 2e édition |
| Editeur : | Boca Raton [Etats-Unis] : Chapman & Hall / CRC, 2006 |
| Collection : | Texts in statiscical science |
| ISBN/ISSN/EAN : | 978-1-58488-587-0 |
| Format : | XVII, 323 p. / ill. / 24 cm |
| Note générale : | Bibliogr. p. [221]-234. - Index |
| Langues : | Anglais |
| Index. décimale : | 519.21 (Théorie des probabilités.Processus stochastiques) |
| Tags : | Bayesian statistical decision theory Markov processes Monte Carlo method Statistique bayésienne Markov, Processus de Monte-Carlo, |
| Résumé : | Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists. |
| Note de contenu : |
Summary :
1- stochastic simulation 2- Bayesian inference 3- Approximte methods of inference 4- Markov chains 5- Gibbs sampling 6- Metropolis-hastings algorithms 7- Further topics in MCMC |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Etat_Exemplaire |
|---|---|---|---|---|---|
| 519.21 GAM | Papier | Bibliothèque Centrale | Mathématiques | Disponible | Consultation sur place |
