# Bayesian temporal biclustering with applications to multi-subject neuroscience studies

arXiv: https://arxiv.org/abs/2406.17131 · HTML: https://mlsanity.com/paper/2406.17131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process](https://mlsanity.com/paper/1604.03192/) (2016)
- [Model-based clustering based on sparse finite Gaussian mixtures](https://mlsanity.com/paper/1606.06828/) (2016)
- [Search Algorithms and Loss Functions for Bayesian Clustering](https://mlsanity.com/paper/2105.04451/) (2021)
- [Dependent Modeling of Temporal Sequences of Random Partitions](https://mlsanity.com/paper/1912.11542/) (2019)
- [Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis](https://mlsanity.com/paper/2009.07745/) (2020)
- [Separate Exchangeability as Modeling Principle in Bayesian Nonparametrics](https://mlsanity.com/paper/2112.07755/) (2021)
- [Bayesian longitudinal tensor response regression for modeling neuroplasticity](https://mlsanity.com/paper/2309.10065/) (2023)
- [Conditional partial exchangeability: a probabilistic framework for multi-view clustering](https://mlsanity.com/paper/2307.01152/) (2023)
- [Semiparametric Latent ANOVA Model for Event-Related Potentials](https://mlsanity.com/paper/2311.01287/) (2023)
- [Product Centered Dirichlet Processes for Bayesian Multiview Clustering](https://mlsanity.com/paper/2312.05365/) (2023)

## Abstract (arXiv)

We consider the problem of analyzing multivariate time series collected on multiple subjects, 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, we propose a Bayesian model for temporal biclustering featuring nested partitions, where a time-invariant partition of subjects induces a time-varying partition of measurements. Our approach allows for data-driven determination of the number of subject 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, we derive a blocked update of measurements' cluster-assignment sequences. We illustrate the performance of our model 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.