• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models
【24h】

Adaptive particle allocation in iterated sequential Monte Carlo via approximating meta-models

机译：通过近似元模型在迭代顺序蒙特卡洛中进行自适应粒子分配

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sequential Monte Carlo (SMC) filters (also known as particle filters) are widely used in the analysis of non-linear and non-Gaussian time series models in diverse application areas such as engineering, finance, and epidemiology. When a time series contains an observation that is very unlikely given the previous observations, evaluation of its conditional log likelihood by SMC can suffer from high variance. The presence of one or more such observations can result in poor Monte Carlo estimate of the overall likelihood. In this article, we develop a novel strategy of particle allocation for off-line iterated SMC based filters, in order to reduce the overall variance of the likelihood estimate to enable efficient computation. The complications arising from the intractability of the actual SMC variance is handled via an approximating meta-model, in which we model the SMC errors in the evaluation of conditional log likelihood of the observations as an autoregressive process. We demonstrate numerical results on both simulated and real data sets where adaptive particle allocation results in 54% lower overall variance over the naive equal allocation of particles at all time points in simulations and 53 % lower variance on a real time series model of epidemic malaria transmission. The approximating model approach presented in this article is novel in the context of SMC and offers a computationally attractive procedure for practical analysis of a broad class of time series models.
机译：顺序蒙特卡洛（SMC）过滤器（也称为粒子过滤器）广泛用于分析工程，金融和流行病学等各种应用领域中的非线性和非高斯时间序列模型。当时间序列包含的观察值与先前的观察值相比不太可能时，SMC对条件对数可能性的评估可能会产生较大的方差。一个或多个这样的观察的存在可能导致整体可能性的蒙特卡洛估计不佳。在本文中，我们为基于离线迭代SMC的滤波器开发了一种新颖的粒子分配策略，以减少似然估计的总体方差，从而实现有效的计算。由实际SMC方差的难处理性引起的复杂性通过近似元模型处理，在该模型中，我们将SMC误差建模为以自回归过程评估条件条件对数似然的可能性。我们在模拟和真实数据集上均显示了数值结果，其中自适应粒子分配的结果是，在模拟中的所有时间点上，相对于天真地均匀分配粒子而言，总方差降低了54％，而在流行病疟疾传播的实时序列模型中，其总方差降低了53％ 。本文介绍的近似模型方法在SMC上下文中是新颖的，它为广泛的时间序列模型的实际分析提供了一种有吸引力的计算程序。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号