This tutorial was held within the Maths Club.
Topics covered
  • Monte Carlo Integration
  • Markov Chain
  • Markov Chain Monte Carlo Sampling
  • Metropolis-Hastings Algorithm
  • Gibbs Sampling
  • Reversible Jump Markov Chain Monte Carlo
  • Simulated MCMC
    • Tutorial slides: pps
    supplementary material
  • Perron-Frobenius Theorem - Matlab Code ReadMe
  • MCMC Demo - Matlab Code ReadMe
    • references
  • Andrieu, C., N. de Freitas, et al. (2003). An introduction to MCMC for machine learning. Machine Learning 50: 5-43
  • Zhu, Dalleart and Tu (2005). Tutorial: Markov Chain Monte Carlo for Computer Vision. Int. Conf on Computer Vision (ICCV) http://civs.stat.ucla.edu/MCMC/MCMC_tutorial.htm
  • Chib, S. and E. Greenberg (1995). Understanding the Metropolis-Hastings Algorithm. The American Statistician 49(4): 327-335.
  • Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1): 97-109.
  • Smith, K. (2007). Bayesian Methods for Visual Multi-object Tracking with Applications to Human Activity Recognition. Ecole Polytechnique Federale de Lausanne (EPFL). PhD: 272
  • Green, P. (2003). Trans-dimensional Markov chain Monte Carlo. Highly structured stochastic systems. P. Green, N. Lid Hjort and S. Richardson. Oxford, Oxford University Press.