Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models

被引：31

作者：

Bergman, N

Doucet, A

Gordon, N

机构：

[1] Linkoping Univ, Div Automat Control, S-58183 Linkoping, Sweden

[2] Univ Cambridge, Dept Engn, Signal Proc Grp, Cambridge CB2 1PZ, England

[3] Def Evaluat & Res Agcy, Malvern WR14 3PS, Worcs, England

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2001年 / 53卷 / 01期

关键词：

optimal estimation; Bayesian inference; sequential Monte Carlo methods; posterior Cramer-Rao bounds;

D O I：

10.1023/A:1017920621802

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：

Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Second, we derive simple recursions to compute posterior Cramer-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given.

引用

页码：97 / 112

页数：16