Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state space models
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We propose a novel blocked version of the continuous-time bouncy particle sampler of Bouchard- Côté et al. [2018] applicable to any differentiable probability density. Motivated by Singh et al. [2017], we also introduce an alternative implementation that leads to significant improvement in terms of effective sample size per second, and furthermore allows for parallelization at the cost of an extra logarithmic factor. The new algorithms are particularly efficient for latent state inference in high-dimensional state space models, where blocking in both space and time is necessary to avoid degeneracy of the proposal kernel. The efficiency of the blocked bouncy particle sampler, in comparison with both the standard implementation of the bouncy particle sampler and the particle Gibbs algorithm of Andrieu et al. [2010], is illustrated numerically for both simulated data and a challenging real-world financial dataset.
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