On the use of backward simulation in the particle Gibbs sampler
F Lindsten, TB Schön - 2012 IEEE International Conference on …, 2012 - ieeexplore.ieee.org
The particle Gibbs (PG) sampler was introduced in [1] as a way to incorporate a particle filter
(PF) in a Markov chain Monte Carlo (MCMC) sampler. The resulting method was shown to
be an efficient tool for joint Bayesian parameter and state inference in nonlinear, non-
Gaussian state-space models. However, the mixing of the PG kernel can be very poor when
there is severe degeneracy in the PF. Hence, the success of the PG sampler heavily relies
on the, often unrealistic, assumption that we can implement a PF without suffering from any …
(PF) in a Markov chain Monte Carlo (MCMC) sampler. The resulting method was shown to
be an efficient tool for joint Bayesian parameter and state inference in nonlinear, non-
Gaussian state-space models. However, the mixing of the PG kernel can be very poor when
there is severe degeneracy in the PF. Hence, the success of the PG sampler heavily relies
on the, often unrealistic, assumption that we can implement a PF without suffering from any …
[CITATION][C] On the use of backward simulation in the particle Gibbs sampler. Acoustics, Speech and Signal Processing (ICASSP)
F Lindsten, T Schön - 2012 IEEE International Conference on. IEEE, 2012
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