Particle filters (PFs) are powerful sampling based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probabil ity distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this pa per, we show how we can exploit the structure of the DBN to increase the efficiency of parti cle filtering, using a technique known as Rao Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite di mensional optimal filter. We show that Rao Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial ba sis function networks and robot localization and map building. We also discuss other potential ap plication areas and provide references to some fi nite dimensional optimal filters.
One solution to this problem is the Rao-Blackwellised particle filter REF .
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
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