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
收藏
页码：97 / 112
页数：16
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