Syllabus
1. Examples of simulation methods.
2. Bayesian statistics, hierarchical models.
3. Examples of MCMC algorithms, Gibbs sampler, Metropolis-Hastings algorithm.
4. Theory of Markov chains with general state space.
5. Ergodicity of MCMC algorithms.
6. Practical aspects and estimation of limit variance.
7. Metropolis-Hastings-Green algorithm.
8. Point processes, birth-death Metropolis-Hastings algorithm.
9. Further applications.
Annotation
Markov chains with general state space, geometric ergodicity.
Gibbs sampler, Metropolis-Hastings algorithm, properties and applications.