Chapter: Sampling from Applied Probability Models

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Home > Introduction to Probability Simulation and Gibbs Sampling with R > Sampling from Applied Probability Models

Introduction to Probability Simulation and Gibbs Sampling with R |

Eric A. Suess; Bruce E. Trumbo

Springer New York

Introduction to Probability Simulation and Gibbs Sampling with R

Sampling from Applied Probability Models

Chapter Info

Authors

Eric A. Suess; Bruce E. Trumbo

ARI Id

1664088987574_291543

Access

Not Available Free

Pages

87-117

DOI

10.1007/978-0-387-68765-0_4

Chapter URL

https://rd.springer.com/chapter/10.1007/978-0-387-68765-0_4

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		i-xiii
Introductory Examples: Simulation, Estimation, and Graphics	Eric A. Suess; Bruce E. Trumbo	1-22
Generating Random Numbers	Eric A. Suess; Bruce E. Trumbo	23-49
Monte Carlo Integration and Limit Theorems	Eric A. Suess; Bruce E. Trumbo	51-85
Sampling from Applied Probability Models	Eric A. Suess; Bruce E. Trumbo	87-117
Screening Tests	Eric A. Suess; Bruce E. Trumbo	119-138
Markov Chains with Two States	Eric A. Suess; Bruce E. Trumbo	139-158
Examples of Markov Chains with Larger State Spaces	Eric A. Suess; Bruce E. Trumbo	159-193
Introduction to Bayesian Estimation	Eric A. Suess; Bruce E. Trumbo	195-218
Using Gibbs Samplers to Compute Bayesian Posterior Distributions	Eric A. Suess; Bruce E. Trumbo	219-248
Using WinBUGS for Bayesian Estimation	Eric A. Suess; Bruce E. Trumbo	249-274
Appendix: Getting Started with R	Eric A. Suess; Bruce E. Trumbo	275-299
Back Matter		301-307
Chapters/Headings	Author(s)	Pages	Info

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