Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems
Abstract
In this paper, we propose a novel model called Recurrent Explicit Duration Switching Linear Dynamical Systems (REDSLDS) that incorporates recurrent explicit duration variables into the rSLDS model. We also propose an inference and learning scheme that involves the use of P{ó}lya-gamma augmentation. We demonstrate the improved segmentation capabilities of our model on three benchmark datasets, including two quantitative datasets and one qualitative dataset.
Cite
Text
Słupiński. "Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Słupiński. "Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/supinski2025aistats-bayesian/)BibTeX
@inproceedings{supinski2025aistats-bayesian,
title = {{Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems}},
author = {Słupiński, Mikołaj},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {100-108},
volume = {258},
url = {https://mlanthology.org/aistats/2025/supinski2025aistats-bayesian/}
}