# Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems

arXiv: https://arxiv.org/abs/2411.04280 · HTML: https://mlsanity.com/paper/2411.04280/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Bayesian Workflow](https://mlsanity.com/paper/2011.01808/) (2020)
- [Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling](https://mlsanity.com/paper/1811.12386/) (2018)
- [Dependent Multinomial Models Made Easy: Stick Breaking with the P'olya-Gamma Augmentation](https://mlsanity.com/paper/1506.05843/) (2015)
- [Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems](https://mlsanity.com/paper/1910.09588/) (2019)
- [Deep Explicit Duration Switching Models for Time Series](https://mlsanity.com/paper/2110.13878/) (2021)
- [Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems](https://mlsanity.com/paper/2410.10653/) (2024)
- [Holdout predictive checks for Bayesian model criticism](https://mlsanity.com/paper/1908.00882/) (2019)
- [Explicit-Duration Markov Switching Models](https://mlsanity.com/paper/1909.05800/) (2019)
- [Switching Autoregressive Low-rank Tensor Models](https://mlsanity.com/paper/2306.03291/) (2023)

## 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.