Bayesian reconstruction of chaotic dynamical systems

被引：59
|
作者：
Meyer, R [1 ]
Christensen, N
机构：
[1] Univ Auckland, Dept Stat, Auckland 1, New Zealand
[2] Carleton Coll, Northfield, MN 55057 USA
来源：
PHYSICAL REVIEW E | 2000年 / 62卷 / 03期
关键词：
D O I：
10.1103/PhysRevE.62.3535
中图分类号：
O35 [流体力学]; O53 [等离子体物理学];
学科分类号：
070204 ; 080103 ; 080704 ;
摘要：
We present a Bayesian approach to the problem of determining parameters of nonlinear models from time series of noisy data. Recent approaches to this problem have been statistically flawed. By applying a Markov chain Monte Carlo algorithm, specifically the Gibbs sampler, we estimate the parameters of chaotic maps. A complete statistical analysis is presented, the Gibbs sampler method is described in detail, and example applications are presented.
引用
收藏
页码：3535 / 3542
页数：8
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