Particle filtering is a standard Monte-Carlo approach for a wide range of sequential inference tasks. The key component of a particle filter is a set of particles with importance weights that serve as a proxy of the true posterior distribution of some stochastic process. In this work, we propose continuous latent particle filters, an approach that extends particle filtering to the continuous-time domain. We demonstrate how continuous latent particle filters can be used as a generic plug-in replacement for inference techniques relying on a learned variational posterior. Our experiments with different model families based on latent neural stochastic differential equations demonstrate superior performance of continuous-time particle filtering in inference tasks like likelihood estimation and sequential prediction for a variety of stochastic processes.
翻译:粒子过滤是一种标准的蒙特- 卡洛方法,用于一系列广泛的顺序推断任务。粒子过滤器的关键组成部分是一组具有重要重量的粒子,这些粒子可以替代某些随机过程的真实后部分布。在这项工作中,我们提议了连续潜质粒子过滤器,将粒子过滤器延伸到连续时间域。我们演示了连续潜质粒子过滤器如何作为依赖一个已学的变异后部的推断技术的通用插件替代物。我们根据潜在的神经神经切分异方程式对不同模型组的实验显示了连续时间粒子过滤任务在推断工作中的优异性表现,如对各种随机过程的可能性估计和顺序预测。