论文标题
通过平均许多粒子过滤器的输出来近似后验预测分布
Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
论文作者
论文摘要
本文介绍了{\ it粒子群滤波器}（不要与粒子群优化混淆）：一种递归且令人尴尬的并行算法，该算法针对与许多粒子的平均期望值近似值近似于后验预测分布的序列。提供了大数量和中心限制定理的定律，以及来自随机波动率模型的模拟数据的数值研究。
This paper introduces the {\it particle swarm filter} (not to be confused with particle swarm optimization): a recursive and embarrassingly parallel algorithm that targets an approximation to the sequence of posterior predictive distributions by averaging expectation approximations from many particle filters. A law of large numbers and a central limit theorem are provided, as well as an numerical study of simulated data from a stochastic volatility model.
