DESCRIPTION:
1. Introduction.
2. Random variable generation.
3. Monte Carlo integration.
4. Controling Monte Carlo variance.
5. Monte Carlo optimization.
6. Markov chains.
7. The metropolis - Hastings algorithm.
8. The slice sampler.
9. The two-stage Gibbs sampler.
10. The multi-stage Gibbs sampler.
11. Variable dimension models and reversible jump algorithms.
12. Diagnosing convergence.
13. Perfect sampling.
14. Iterated and sequential importance sampling.
A. Probability distributions.
B. Notation.