Advertisement

Improved Particle Filters and Smoothing

  • Photis Stavropoulos
  • D. M. Titterington
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Exact recursive Bayesian inference is essentially impossible except in very special scenarios, such as the linear-Gaussian dynamic systems that are amenable to the Kalman filter and associated methods. Otherwise, some form of approximation is necessary. In some contexts, a parametric approximation might still be workable, as in (Titterington 1973)’s use of two-component Normal mixtures in a simple extremum-tracking problem (which we revisit later in this chapter), but nowadays it is more common to carry forward, as an estimate of the current distribution of the items of interest, what is claimed to be a simulated sample from that distribution, in other words, a particle filter.

Keywords

Posterior Distribution Relative Entropy Kernel Density Estimate Variance Matrix Kernel Sampler 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Photis Stavropoulos
  • D. M. Titterington

There are no affiliations available

Personalised recommendations