A Quasi Monte Carlo Approach to Piecewise Linear Markov Approximations of Markov Operators

Ding Jiu, Mao Dong and Zhou Aihui
Additional contact information
Ding Jiu: 1. Department of Mathematics, The University of Southern Mississippi, Hattiesburg, MS 39406-5045, USA.
Mao Dong: 2. Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China.
Zhou Aihui: 3. Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China.

Monte Carlo Methods and Applications, 2003, vol. 9, issue 4, 295-309

Abstract: In this paper, we address the computational issue of approximating Markov operators which are widely used in the stochastic study of chaotic dynamical systems. We will concentrate on a quasi Monte Carlo implementation of piecewise linear Markov approximations that preserve the Markov structure.

Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/156939603322601932 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:9:y:2003:i:4:p:295-309:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/156939603322601932

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().