Introduction to probability simulation and Gibbs sampling with R
The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial...
Uloženo v:
| Hlavní autoři: | , |
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2010. |
| Edice: | Use R!
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| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Introduction to probability simulation and Gibbs sampling with R, Eric A. Suess, Bruce E. Trumbo, New York (N. Y.), Springer, 2010, 1 vol. (XIII-307 p.), Use R!, 978-0-387-40273-4 • Introduction to probability simulation and Gibbs sampling with R, Eric A. Suess, Bruce E. Trumbo, New York (N. Y.), Springer, 2010, 1 vol. (XIII-307 p.), Use R!, 978-0-387-40273-4 • Introduction to Probability Simulation and Gibbs Sampling with R, Texte imprimé, 9781441906939 |
Obsah:
- Introductory Examples: Simulation, Estimation, and Graphics Generating Random Numbers Monte Carlo Integration and Limit Theorems Sampling from Applied Probability Models Screening Tests Markov Chains with Two States Examples of Markov Chains with Larger State Spaces to Bayesian Estimation Using Gibbs Samplers to Compute Bayesian Posterior Distributions Using WinBUGS for Bayesian Estimation Appendix: Getting Started with R