- Week 2: Motivate simulation. Inversion. Rejection methods. Chapter 1 Gamerman & Lopes
- Week 3: Rejection and resampling. Common multivariate distributions. Chapter 1 Gamerman & Lopes (R-code examples: Inversion, Transformation, Rejection, MC_and_ANALYTIC_INTEGRAL,Multivar_normal . Essentials: Repetition)
- Week 4-5: Some Bayes theory. Asymptotic expansions. Monte Carlo integration. Chapter 2.1-2 and 3 Gamerman & Lopes Monte Carlo estimation . Essentials: Repetition)
- Week 5-6: Exercise 1
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- Week 7: Bayes theory. Markov chains. Chapter 2 and 4 Gamerman & Lopes
- Week 8: Gibbs sampling. Chapter 5 Gamerman & LopesGibbs sampler in graph . Overview/Example: Repetition)
- Week 9: Metropolis Hastings. Chapter 6 Gamerman & Lopes. Overview/Example: Repetition)
- Week 10-11: Exercise 2
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- Week 12: Bootstraping.
- Week 13: Boostrapping. (Repetition) Permutation tests. Cross-validation.
- Week 14: Easter break (No teaching Tuesday 3 April, Friday 6 April, Tuesday 10 April.)
- Week 15-16: (Repetition). Kernel estimation and classification.
- Week 16-17: Exercise 3