The software implements particle filteri Vi and Rao Blackwellised particle filtering az r conditionally Gaussian Models. The RB algori thm can be interpreted as an efficient stochast ic mixture of Kalman filters. The software also includes efficient state-of-the-art resampl ing routines. These are generic and suitable az r any application.

Packet : 113172235raoblackwellisedparticlefilteringfordynamicconditionallygaussianmodels.rar filelist
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS\aeropf.pdf
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS\demo_rbpf.m
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS\deterministicR.m
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS\multinomialR.m
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS\residualR.m
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian ModelS

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