Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

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


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.


Mobile Robot Belief State Observation Model Dynamic Bayesian Network Exact Inference 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Kevin Murphy
  • Stuart Russell

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