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

Searching multiregression dynamic models of resting-state fMRI networks using integer programming

Tools
- Tools
+ Tools

Costa, Lilia, Smith, J. Q., Nichols, Thomas E., Cussens, James, Duff, Eugene P. and Makin, Tamar R. (2015) Searching multiregression dynamic models of resting-state fMRI networks using integer programming. Bayesian Analysis, 10 (2). pp. 441-478. doi:10.1214/14-BA913 ISSN 1931-6690.

[img]
Preview
PDF
WRAP_Costa_BA913.pdf - Published Version - Requires a PDF viewer.

Download (3325Kb) | Preview
Official URL: http://dx.doi.org/10.1214/14-BA913

Request Changes to record.

Abstract

A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the hypothesised relationships accommodate conditional conjugate inference. We demonstrate for the first time how straightforward it is to search over all possible connectivity networks with dynamically changing intensity of transmission to find the MAP model within this class. This search method is made feasible by using a novel application of an Integer Programming algorithm. The efficacy of applying this particular class of dynamic models to this domain is shown and more specifically the computational efficiency of a corresponding search of 11-node DAG model space. We proceed to show how diagnostic methods, analogous to those defined for static Bayesian Networks, can be used to suggest embellishment of the model class to extend the process of model selection. All methods are illustrated using simulated and real resting-state functional Magnetic Resonance Imaging (fMRI) data.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Multivariate analysis -- Graphic methods, Magnetic resonance microscopy, Integer programming
Journal or Publication Title: Bayesian Analysis
Publisher: International Society for Bayesian Analysis
ISSN: 1931-6690
Official Date: 2015
Dates:
DateEvent
2015Published
28 October 2014Available
Volume: 10
Number: 2
Number of Pages: 40
Page Range: pp. 441-478
DOI: 10.1214/14-BA913
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 28 July 2016
Date of first compliant Open Access: 28 July 2016
Related URLs:
  • Publisher

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