Sequential Monte Carlo (SMC) methods

This collection of functions implements some commonly used particle filters and particle smoother for state estimation in state-space models. The examples only serve as introductionary examples of the methods and the problem can be solved in closed form using Kalman methods, since the system is linear and Gaussian. 


This code is written by Johan Dahlin and it is available here, see also Johan’s software page

Relevant papers

T.B. Schön and F. Lindsten, Learning of dynamical systems - Particle filters and Markov chain methods. Manuscript, 2014.

A. Doucet and A. M. Johansen, A tutorial on particle filtering and smoothing: fifteen years later, in The Oxford Handbook of Nonlinear Filtering, D. Crisan and B. Rozovsky (Eds.), Oxford University Press, 2011.

M. Briers, A. Doucet, and S. Maskell, Smoothing algorithms for state-space models, in Annals of the Institute of Statistical Mathematics, vol 62, pp. 61-89, 2010.

 © Thomas Schön 2022