sequential Monte Carlo

(2.3 hours to learn)


Sequential Monte Carlo is a general framework for Monte Carlo algorithms which involve sampling from a sequence of distributions. It encompasses sequential importance sampling, particle filters, and annealed importance sampling as special cases.


This concept has the prerequisites:


  • Learn the general formulation of sequential Monte Carlo samplers
  • Understand why each of the following operators preserves weighted samples:
    • importance weighting
    • sampling from the sampling (proposal) distribution
    • resampling
    • rejuvenation
  • Know how to use SMC to estimate the normalizing constant of a distribution
  • What is the optimal sampling (proposal) distribution?
    • Know how this can be approximated using look-ahead

Core resources (read/watch one of the following)


Sequential Monte Carlo samplers
  • Section 1, "Introduction"
  • Section 2, "Sequential Monte Carlo sampling"
Authors: Pierre del Moral,Arnaud Doucet


See also

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