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.

翻译：Scalar-Gibbs 随机分区模型是空间依赖分区进程随机分区的前一种,从而鼓励代表空间毗连区域的群落。此外,还利用Bayesian 缩微镜缩微镜前科来确定与预测最相关的共变量和区域。 模拟数据集用模拟数据集演示了拟议的模型。