A0784
Title: Bayesian temporal biclustering with applications to multi-subject neuroscience studies
Authors: Michele Guindani - University of California Los Angeles (United States) [presenting]
Marina Vannucci - Rice University (United States)
Erik Sudderth - UC Irvine (United States)
Jaylen Lee - UCI (United States)
Megan Peters - UCI (United States)
Federica Zoe Ricci - Swarthmore College (United States)
Abstract: The problem of analyzing multivariate time series collected on multiple subjects is considered, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associated measurements. To this end, a Bayesian model is proposed for temporal biclustering featuring nested partitions, where a time-invariant partition of subjects induces a time-varying partition of measurements. The approach allows for data-driven determination of the number of subjects and measurement clusters as well as estimation of the number and location of changepoints in measurement partitions. To efficiently perform model fitting and posterior estimation with Markov chain Monte Carlo, a blocked update of measurements' cluster-assignment sequences is derived. The performance of the model is illustrated in two applications to functional magnetic resonance imaging data and to an electroencephalogram dataset. The results indicate that the proposed model can combine information from potentially many subjects to discover a set of interpretable, dynamic patterns. Experiments on simulated data compare the estimation performance of the proposed model against ground-truth values and other statistical methods, showing that it performs well at identifying ground-truth subject and measurement clusters even when no subject or time dependence is present.
