Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms
50 Stars
Updated Last
11 Months Ago
Started In
September 2019


Stable Dev Build Status Coverage Code Style: Blue

AdvancedPS provides an efficient implementation of common particle based Monte Carlo samplers using the AbstractMCMC interface. The package also relies on Libtask for task manipulation. AdvancedPS is part of the Turing ecosystem.


Inside the Julia REPL

julia>] add AdvancedPS


Detailed examples are available in the documentation


  1. Doucet, Arnaud, and Adam M. Johansen. "A tutorial on particle filtering and smoothing: Fifteen years later." Handbook of nonlinear filtering 12, no. 656-704 (2009): 3.

  2. Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. "Particle Markov chain Monte Carlo methods." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 3 (2010): 269-342.

  3. Tripuraneni, Nilesh, Shixiang Shane Gu, Hong Ge, and Zoubin Ghahramani. "Particle gibbs for infinite hidden Markov models." In Advances in Neural Information Processing Systems, pp. 2395-2403. 2015.

  4. Lindsten, Fredrik, Michael I. Jordan, and Thomas B. Schön. "Particle Gibbs with ancestor sampling." The Journal of Machine Learning Research 15, no. 1 (2014): 2145-2184.

  5. Pitt, Michael K., and Neil Shephard. "Filtering via simulation: Auxiliary particle filters." Journal of the American statistical association 94, no. 446 (1999): 590-599.

  6. Doucet, Arnaud, Nando de Freitas, and Neil Gordon. "Sequential Monte Carlo Methods in Practice."

  7. Del Moral, Pierre, Arnaud Doucet, and Ajay Jasra. "Sequential Monte Carlo samplers." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, no. 3 (2006): 411-436.