Introduction to probability simulation and Gibbs sampling with R [electronic resource] / by Eric A. Suess, Bruce E. Trumbo.
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...
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Full Text (via Springer) |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York ; London :
Springer,
2010.
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| Series: | Use R!
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Table of Contents:
- 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
- Introduction to Bayesian Estimation
- Using Gibbs Samplers to Compute Bayesian Posterior Distributions
- Using WinBUGS for Bayesian Estimation
- Appendix: Getting Started with R.