Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Filter based methods for statistical linear inverse problems

Tools
- Tools
+ Tools

Iglesias-Hernandez, Marco, A., Lin, Kui, Lu, Shuai and Stuart, A. M. (2017) Filter based methods for statistical linear inverse problems. Communications in Mathematical Sciences, 15 (7). pp. 1867-1896. doi:10.4310/CMS.2017.v15.n7.a4 ISSN 1539-6746.

[img]
Preview
PDF
WRAP-filter-based-methods-statistical-inverse-problems-Iglesias-2017.pdf - Accepted Version - Requires a PDF viewer.

Download (2408Kb) | Preview
Official URL: http://dx.doi.org/10.4310/CMS.2017.v15.n7.a4

Request Changes to record.

Abstract

Ill-posed inverse problems are ubiquitous in applications. Understanding of algorithms for their solution has been greatly enhanced by a deep understanding of the linear inverse problem. In the applied communities ensemble-based filtering methods have recently been used to solve inverse problems by introducing an artificial dynamical system. This opens up the possibility of using a range of other filtering methods, such as 3DVAR and Kalman based methods, to solve inverse problems, again by introducing an artificial dynamical system. The aim of this paper is to analyze such methods in the context of the linear inverse problem.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Algorithms, Random noise theory, Kalman filtering
Journal or Publication Title: Communications in Mathematical Sciences
Publisher: International Press
ISSN: 1539-6746
Official Date: 16 October 2017
Dates:
DateEvent
16 October 2017Published
6 May 2017Accepted
Volume: 15
Number: 7
Page Range: pp. 1867-1896
DOI: 10.4310/CMS.2017.v15.n7.a4
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 8 December 2017
Date of first compliant Open Access: 8 December 2017
Funder: China. Guo jia ke xue ji shu bu [Ministry of Science and Technology] (CMST)
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Programme Grant EQUIPEngineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDOffice of Naval Researchhttp://dx.doi.org/10.13039/100000006
91130004National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
11522108National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
14QA1400400Science and Technology Commission of Shanghai Municipalityhttp://dx.doi.org/10.13039/501100003399
Grant number B08018Program for New Century Excellent Talents in Universityhttp://dx.doi.org/10.13039/501100004602
2015CB856003Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us