particle filter

(1.8 hours to learn)


The particle filter is a Monte Carlo algorithm for posterior inference in temporal models. The posterior is approximated with a weighted set of discrete particles. In each step, each particle's state is extended according to a proposal distribution, and its weight is updated based on the likelihood of the evidence. The algorithm is useful in robotics and in visual tracking because it doesn't require storing the entire history.


This concept has the prerequisites:


  • Know the steps of the particle filter algorithm.
  • Be aware that an important advantage of the algorithm is that it does not require storing the entire history.
  • Be aware that the following are major sources of variance in the estimator, and that effective performance can depend on the details of the implementation:
    • the proposal distribution
    • the resampling method

Core resources (read/watch one of the following)


Supplemental resources (the following are optional, but you may find them useful)


See also