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We propose a novel blocked version of the continuous-time bouncy particlesampler of [Bouchard-C\^ot\'e et al., 2018] which is applicable to anydifferentiable probability density. This alternative implementation ismotivated by blocked Gibbs sampling for state space models [Singh et al., 2017]and leads to significant improvement in terms of effective sample size persecond, and furthermore, allows for significant parallelization of theresulting algorithm. The new algorithms are particularly efficient for latentstate inference in high-dimensional state space models, where blocking in bothspace and time is necessary to avoid degeneracy of MCMC. The efficiency of ourblocked bouncy particle sampler, in comparison with both the standardimplementation of the bouncy particle sampler and the particle Gibbs algorithmof Andrieu et al. [2010], is illustrated numerically for both simulated dataand a challenging real-world financial dataset.

Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state space models