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Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

  • Kevin Murphy
  • Stuart Russell
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Particle filtering in high dimensional state-spaces can be inefficient because a large number of samples is needed to represent the posterior. A standard technique to increase the efficiency of sampling techniques is to reduce the size of the state space by marginalizing out some of the variables analytically; this is called Rao-Blackwellisation (Casella and Robert 1996). Combining these two techniques results in Rao-Blackwellised particle filtering (RBPF) (Doucet 1998, Doucet, de Freitas, Murphy and Russell 2000). In this chapter, we explain RBPF, discuss when it can be used, and give a detailed example of its application to the problem of map learning for a mobile robot, which has a very large (~ 2100) discrete state space.

Keywords

Mobile Robot Belief State Observation Model Dynamic Bayesian Network Exact Inference 
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.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Kevin Murphy
  • Stuart Russell

There are no affiliations available

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