The Monte Carlo Method: Classic Papers

  • Metropolis, N. and Ulam, S. (1949).
    The Monte Carlo Method. (pdf)
    Journal of the American Statistical Association, 44, 335-341.

  • von Neumann, J. (1951).
    Various Techniques used in Connection with Random Digits.
    National Bureau of Standards, Applied Math Series, 11, 36-38.

  • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N, Teller, A. H., Teller, E. (1953).
    Equations of State Calculations by Fast Computing Machines. (pdf)
    Journal of Chemical Physics, 21, 1087-1092.

  • Marshall, A. W. (1956).
    The Use of Multi-Stage Sampling Schemes in Monte Carlo Computations.
    Symposium on Monte Carlo Methods (Wiley), 123-140.

  • Hastings, W. K. (1970).
    Monte Carlo Sampling Methods using Markov Chains and Their Applications. (pdf)
    Biometrika, 57, 97-109.

  • Kirkpatrick, S., Gelatt, D. and Vecchi, M. P. (1983).
    Optimization by Simulated Annealing. (pdf)
    Science, 220, 671-680.

  • Geman, S. and Geman, D. (1984).
    Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. (pdf)
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.

  • Swendsen, R. and Wang, J.-S. (1987).
    Nonuniversal Critical Dynamics in Monte Carlo Simulations.
    Physical Review Letters, 58, 86-88.

  • Gelfand, A. E. and Smith, A. F. M. (1990).
    Sampling-Based Approaches to Calculating Marginal Densities. (pdf)
    Journal of the American Statistical Association, 85, 398-409.

  • Gordon, N. J., Salmond, D. J. and Smith, A. F. M. (1993).
    Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. (pdf)
    IEE Proceedings-F, 140, 107-113.

  • Pitt, M. K. and Shephard, N. (1999).
    Filtering via Simulation: Auxiliary Particle Filters. (pdf)
    Journal of the American Statistical Association, 94, 590-599.