Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models

Sara K Wade, Mica Teo Shu Xian

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Scalar-on-image regression aims to investigate changes in a scalar response of interest based on high-dimensional imaging data. We propose a novel Bayesian nonparametric scalar-on-image regression model that utilises the spatial coordinates of the voxels to group voxels with similar effects on the response to have a common coefficient. We employ the Potts-Gibbs random partition model as the prior for the random partition in which the partition process is spatially dependent, thereby encouraging groups representing spatially contiguous regions. In addition, Bayesian shrinkage priors are utilised to identify the covariates and regions that are most relevant for the prediction. The proposed model is illustrated using the simulated data sets.
Original languageEnglish
Title of host publicationBAyesian Young Statisticians Meeting 2021
Publication statusAccepted/In press - 23 Jun 2022
EventBAyesian Young Statisticians Meeting 2021 -
Duration: 1 Sep 20213 Sep 2021

Publication series

NameSpringer Proceedings in Mathematics & Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


ConferenceBAyesian Young Statisticians Meeting 2021


Dive into the research topics of 'Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models'. Together they form a unique fingerprint.

Cite this