Switch-Reset Models : Exact and Approximate Inference
Abstract
Reset models are constrained switching latent Markov models in which the dynamics either continues according to a standard model, or the latent variable is resampled. We consider exact marginal inference in this class of models and their extension, the switch-reset models. A further convenient class of conjugate-exponential reset models is also discussed. For a length $T$ time-series, exact filtering scales with $T^2$ squared and smoothing $T^3$ cubed. We discuss approximate filtering and smoothing routines that scale linearly with $T$. Applications are given to change-point models and reset linear dynamical systems.
Cite
Text
Bracegirdle and Barber. "Switch-Reset Models : Exact and Approximate Inference." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Bracegirdle and Barber. "Switch-Reset Models : Exact and Approximate Inference." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/bracegirdle2011aistats-switchreset/)BibTeX
@inproceedings{bracegirdle2011aistats-switchreset,
title = {{Switch-Reset Models : Exact and Approximate Inference}},
author = {Bracegirdle, Chris and Barber, David},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {190-198},
volume = {15},
url = {https://mlanthology.org/aistats/2011/bracegirdle2011aistats-switchreset/}
}