An introduction to Sequential Monte Carlo

Nicolas Chopin and Omiros Papaspiliopoulos

Available here. See software for the accompanying Python library, particles.


  1. Introduction
  2. Introduction to state-space models
  3. Beyond state-space models
  4. Introduction to Markov processes
  5. Feynman-Kac models: definition, properties and recursions
  6. Finite state-spaces and hidden Markov models
  7. Linear-Gaussian state-space models
  8. Importance sampling
  9. Importance resampling
  10. Particle filtering
  11. Convergence and stability of particle filters
  12. Particle smoothing
  13. Sequential quasi-Monte Carlo
  14. Maximum likelihood estimation of state-space models
  15. Markov chain Monte Carlo
  16. Bayesian estimation of state-space models and particle MCMC
  17. SMC samplers
  18. SMC^2, sequential inference in state-space models
  19. Advanced topics and open problems

Typos: see here.