Demonstration of enhanced Monte Carlo computation of the fisher information for complex problems

Author

Xumeng Cao

Author_Institution

Dept. of Appl. Math. & Stat., Johns Hopkins Univ., Baltimore, MD, USA

fYear

2013

fDate

17-19 June 2013

Firstpage

4003

Lastpage

4008

Abstract

The Fisher information matrix summarizes the amount of information in a set of data relative to the quantities of interest. There are many applications of the information matrix in statistical modeling, system identification and parameter estimation. This short paper reviews a feedback-based method and an independent perturbation approach for computing the information matrix for complex problems, where a closed form of the information matrix is not achievable. We show through numerical examples how these methods improve the accuracy of the estimate of the information matrix compared to the basic resampling-based approach. Some relevant theory is summarized.

Keywords

Monte Carlo methods; parameter estimation; statistical analysis; Fisher information matrix; complex problems; enhanced Monte Carlo computation; feedback-based method; independent perturbation approach; parameter estimation; statistical modeling; system identification; Accuracy; Computational modeling; Estimation; Monte Carlo methods; Numerical models; Symmetric matrices; Vectors; Monte Carlo simulation; feedback information; simultaneous perturbation; the Fisher information matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2013

Conference_Location

Washington, DC

ISSN

0743-1619

Print_ISBN

978-1-4799-0177-7

Type

conf

DOI

10.1109/ACC.2013.6580452

Filename

6580452