EM++: A parameter learning framework for stochastic switching systems
<|reference_start|>EM++: A parameter learning framework for stochastic switching systems: 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.<|reference_end|>
@article{wang2024em++:,
    title={EM++: A parameter learning framework for stochastic switching systems},
    author={Renzi Wang, Alexander Bodard, Mathijs Schuurmans, and Panagiotis
  Patrinos},
    journal={arXiv preprint arXiv:2407.16359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16359},
    primaryClass={math.OC cs.SY eess.SY}
}