Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems
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Abstract (arXiv)
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'olya-gamma augmentation. We demonstrate the improved segmentation capabilities of our model on three benchmark datasets, including two quantitative datasets and one qualitative dataset.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 6 Nov 2024.
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References · 9
- 2020Bayesian Workflowcited by 47
- 2018Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modelingcited by 27
- 2019Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systemscited by 26
- 2015Dependent Multinomial Models Made Easy: Stick Breaking with the P'olya-Gamma Augmentationcited by 25
- 2021Deep Explicit Duration Switching Models for Time Seriescited by 17
- 2024Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systemscited by 4
- 2019Holdout predictive checks for Bayesian model criticismcited by 3
- 2019Explicit-Duration Markov Switching Modelscited by 3
- 2023Switching Autoregressive Low-rank Tensor Modelscited by 3
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