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.
翻译:我们提出一个新的封存版本的[Bouchard-C ⁇ ot\'e 等人,2018年]连续充气颗粒取样器,适用于任何不同的概率密度。这一替代应用的动机是州空间模型的封存Gibbs取样器[Singh等人,2017年],并导致每秒有效取样规模的显著改善,此外,还允许由此得出的算法的显著平行化。新的算法对于高维状态空间模型中的潜伏状态推断特别有效,因为在高维状态空间模型中,空间和时间的屏蔽对于避免MCMC的退化都是必要的。我们被封存的充气颗粒取样器的效率,与软体颗粒取样器的标准应用和Andrieu等人的粒子Gibbus算法相比,[2010年]对于模拟数据和具有挑战性的现实世界金融数据集都是数字性的。