Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems

Mikołaj Słupiński
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:100-108, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v258-slupinski25a, title = {Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems}, author = {S{\l}upi{\'n}ski, Miko{\l}aj}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {100--108}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/slupinski25a/slupinski25a.pdf}, url = {https://proceedings.mlr.press/v258/slupinski25a.html}, 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.} }
Endnote
%0 Conference Paper %T Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems %A Mikołaj Słupiński %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-slupinski25a %I PMLR %P 100--108 %U https://proceedings.mlr.press/v258/slupinski25a.html %V 258 %X 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.
APA
Słupiński, M.. (2025). Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:100-108 Available from https://proceedings.mlr.press/v258/slupinski25a.html.

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