On sequential Monte Carlo sampling methods for Bayesian filtering
On sequential Monte Carlo sampling methods for Bayesian filtering
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DOI：
10.1023/a:1008935410038
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发表时间：
2000-07-01
影响因子：
2.2
通讯作者：
Andrieu, C
中科院分区：
文献类型：
--
作者：
Doucet, A;Godsill, S;Andrieu, C
在本文中，我们介绍了从后分布的顺序仿真方法的概述。对于通常是非线性和非高斯的离散时间动态模型，这些方法在贝叶斯过滤中特别感兴趣。开发了一个普遍重要的抽样框架，该框架统一了过去几十年来在几个不同的科学学科中提出的许多方法。还提出了对现有方法的新型扩展。我们特别展示了如何合并与以前在确定性过滤文献中使用的局部线性化方法相似的；这些导致非常有效的重要性分布。此外，我们描述了一种使用rao-blackwellisation的方法，以利用一些重要类别的状态空间模型中存在的分析结构。在最后一部分中，我们开发了用于预测，平滑和评估动态模型中可能性的算法。
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.