# EM++: A parameter learning framework for stochastic switching systems

arXiv: https://arxiv.org/abs/2407.16359 · HTML: https://mlsanity.com/paper/2407.16359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach](https://mlsanity.com/paper/2411.08232/) (2024)

## Abstract (arXiv)

This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of distributions, such as Gaussian distributions, this algorithm reduces to the well-known expectation-maximization method. We prove global convergence of the algorithm under suitable assumptions, thus addressing an important open issue in the switching system identification literature. The effectiveness of both the proposed model and algorithm is validated through extensive numerical experiments.