This page contains resources about Monte Carlo Methods, Sampling Methods, Monte Carlo Inference, Stochastic Simulation, Systems Simulation and Computational Modelling.

Subfields and ConceptsEdit

  • Monte Carlo techniques
    • Particle Filtering / Sequential Monte Carlo (SMC)
    • Kalman Filtering
    • Importance Sampling
    • Sequential Importance Sampling
    • Rejection Sampling
    • Rao-Blackwellised Particle Filtering (RBPF)
    • Markov Chain Monte Carlo (MCMC)
      • Gibbs Sampling
      • Metropolis–Hastings (MH) Algorithm
      • MH-in-Gibbs / Variable-at-a-time / Metropolis-within-Gibbs / MH-within-Gibbs
      • Hybrid / Hamiltonian Monte Carlo (HMC)
      • No-U-Turn Sampler (NUTS)
    • Simulated Annealing
    • Annealed Importance Sampling
    • Cross-entropy (CE) Method
  • Variance Reductions Techniques (VRT)
    • Antithetic Variables
    • Control variates / Regression sampling
    • Importance Sampling
  • Simulation and Computational Modelling

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit

Books and Book ChaptersEdit

  • Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method. 3rd Ed. John Wiley & Sons.
  • Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). "Chapter 17: Monte Carlo Methods". Deep Learning. MIT Press.
  • Theodoridis, S. (2015). "Chapter 14: Monte Carlo Methods". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Theodoridis, S. (2015). "Chapter 17: Particle Filtering". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Law, A. M., Kelton (2014). Simulation modeling and analysis. 5th Ed. McGraw-Hill.
  • Owen, A. B. (2013). Monte Carlo Theory, Methods and Examples. (link)
  • Durbin, J., & Koopman, S. J. (2012). Time series analysis by state space methods. Oxford University Press.
  • Murphy, K. P. (2012). "Chapter 23: Monte Carlo inference". Machine Learning: A Probabilistic Perspective. MIT Press.
  • Barber, D. (2012). "Chapter 27: Sampling". Bayesian Reasoning and Machine Learning. Cambridge University Press.
  • Ross, S. M. (2012). Simulation. 5th Ed. Academic Press.
  • Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (Eds.). (2011). Handbook of Markov Chain Monte Carlo. CRC press.
  • Kroese, D. P., Taimre, T., & Botev, Z. I. (2011). Handbook of monte carlo methods. John Wiley & Sons.
  • Robert, C., & Casella, G. (2010). Monte Carlo statistical methods. Springer Science & Business Media.
  • Koller, D., & Friedman, N. (2009). "Chapter 12: Particle-Based Approximate Inference". Probabilistic Graphical Models. MIT Press.
  • Asmussen, S., & Glynn, P. W. (2007). Stochastic simulation: algorithms and analysis. Springer Science & Business Media.
  • Bishop, C. M. (2006). "Chapter 11: Sampling Methods". Pattern Recognition and Machine Learning. Springer.
  • MacKay, D. J. (2003). "Chapter 29: Monte Carlo Methods" Information Theory, Inference and Learning Algorithms. Cambridge University Press.
  • Rubinstein, R. Y., & Melamed, B. (1998). Modern simulation and modeling. John Wiley & Sons.
  • Gilks, W. R., Richardson, S. & Spiegelhalter, D. J. (eds): Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, 1996.

Scholarly ArticlesEdit

  • Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods.
  • MacKay, D. J. (1998). Introduction to monte carlo methods. In Learning in graphical models (pp. 175-204). Springer Netherlands.



See alsoEdit

Other ResourcesEdit

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