  We propose a novel blocked version of the continuous-time bouncy particle
sampler of Bouchard- C\^ot\'e 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.
