Bayesian model evidence for order selection and correlation testing

Leigh Andrea Johnston, Iven Michiel Yvonne Mareels, Gary Francis Egan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data. Secondly, an evidence-based method for testing change in linear correlation between datasets is proposed, which is demonstrated to outperform both the traditional statistical test of the null hypothesis of no correlation change and the likelihood ratio test.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
EditorsNigel Lovell
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)1557-170X
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2011 - Boston, United States of America
Duration: 30 Aug 20113 Sept 2011
Conference number: 33rd (Proceedings)


ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2011
Abbreviated titleEMBC 2011
Country/TerritoryUnited States of America
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