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
- 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"
→ Monte Carlo Strategies in Scientific Computing (2001)
A monograph on Monte Carlo methods.
Location: Section 3.4, "A general framework," pages 67-76
- Sections 3.1 through 3.3 discuss instructive examples of the framework.
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