Rao-Blackwellized Particle Smoothing for Conditionally Linear Gaussian Models (NOTICE CHANGED TIME!)

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• Dr Simo Sarkka, Biomedical Engineering and Computer Science Dept, Aalto University, Finland
• Wednesday 14 December 2011, 14:00-14:45
• Cambridge University Engineering Department, Lecture Room 6.

If you have a question about this talk, please contact Rachel Fogg.

Although Monte Carlo based particle filters and smoothers can be used for approximate inference in almost any kind of probabilistic state space models, the required number of samples for a sufficient accuracy can be high. The efficiency of sampling can be improved by Rao-Blackwellization, where part of the state is marginalized out in closed form, and only the remaining part is sampled. Because the sampled space has a lower dimension, fewer particles are required. In this talk I will discuss on Rao-Blackwellization in the context of conditionally linear Gaussian models, and present efficient Rao-Blackwellized versions of previously proposed particle smoothers.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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