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)
2011-05-20, finnkrl