MONTE CARLO METHODS FOR BACKWARD EQUATIONS IN NONLINEAR FILTERING

被引：0

作者：

Milstein, G. N. ^{[1
]}

Tretyakov, M. V. ^{[2
]}

机构：

[1] Ural State Univ, Ekaterinburg 620083, Russia

[2] Univ Leicester, Dept Math, Leicester LE1 7RH, Leics, England

来源：

ADVANCES IN APPLIED PROBABILITY | 2009年 / 41卷 / 01期

基金：

英国工程与自然科学研究理事会;

关键词：

Pathwise filtering equation; stochastic partial differential equation; Monte Carlo technique; Kallianpur-Striebel formula; mean-square and weak numerical methods; PARTICLE APPROXIMATION;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We consider Monte Carlo methods for the classical nonlinear filtering problem. The first method is based on a backward pathwise filtering equation and the second method is related to a backward linear stochastic partial differential equation. We study convergence of the proposed numerical algorithms. The considered methods have Such advantages as a capability in principle to solve filtering problems of large dimensionality, reliable error control, and recurrency. Their efficiency is achieved due to the numerical procedures which use effective numerical schemes and variance reduction techniques. The results obtained are supported by numerical experiments.

引用

页码：63 / 100

页数：38

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