Curriculum
- The curriculum consists of what is discussed in the lectures and the exercises. The topics discussed in the lectures are also covered by the following texts:
- Stochastic simulation (direct methods and MCMC): D. Gamerman and H.F. Lopes (2006). Markov chain Monte Carlo - Stochastic simulation for Bayesian inference. Chapman and Hall, London.
- Section 1: All.
- Section 2: 2.1 - 2.4.
- Section 3: 3.4, 3.5 (except 3.5.3).
- Section 4: All (most of this material should be known from TMA4265 Stochastic processes).
- Section 5: 5.1 - 5.4, 5.5 (except 5.5.2, 5.5.3).
- Section 6: 6.1 - 6.4.
- Kernel methods and classification: T. Hastie, R. Tibshirani and J. Friedman (2001). The elements of statistical learning - Data mining, inference and prediction, Springer: http://www-stat.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf
- Section 4: 4.1, 4.3 (except 4.3.3).
- Section 6: 6.8.
- Section 8: 8.5 (except 8.5.3)
- Section 13: 13.1, 13.2.1, 13.2.3
- Section 14: 14.3.6, 14.3.7
- Bootstrapping and cross validation: B. Efron and R.J. Tibshirani (1993). An introduction to the bootstrap. Chapman and Hall, London.
- Chapters 1 to 3: All (most of this material should be known from before).
- Chapters 4, 5, 6: All.
- Chapter 8: 8.1 - 8.6.
- Chapter 9: 9.1 - 9.6.
- Chapter 10: 10.1 - 10.4.
- Chapter 12: 12.1 - 12.5.
- Chapter 13: 13.1 - 13.3.
- Chapter 15.
- Chapter 17.
- Cleve's Corner 1995: Random thoughts (Uniform random numbers in Matlab)
- Cleve's Corner 2001: Normal Behavior (Gaussian random numbers in Matlab)