  We propose a novel blocked version of the continuous-time bouncy particle
sampler of [Bouchard-C\^ot\'e et al., 2018] which is applicable to any
differentiable probability density. This alternative implementation is
motivated by blocked Gibbs sampling for state space models [Singh et al., 2017]
and leads to significant improvement in terms of effective sample size per
second, and furthermore, allows for significant parallelization of the
resulting algorithm. 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 MCMC. The efficiency of our
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.
