Links Between Multiplicity Automata, Observable Operator Models and Predictive State Representations -- a Unified Learning Framework
Abstract
Stochastic multiplicity automata (SMA) are weighted finite automata that generalize probabilistic automata. They have been used in the context of probabilistic grammatical inference. Observable operator models (OOMs) are a generalization of hidden Markov models, which in turn are models for discrete-valued stochastic processes and are used ubiquitously in the context of speech recognition and bio-sequence modeling. Predictive state representations (PSRs) extend OOMs to stochastic input-output systems and are employed in the context of agent modeling and planning.
Cite
Text
Thon and Jaeger. "Links Between Multiplicity Automata, Observable Operator Models and Predictive State Representations -- a Unified Learning Framework." Journal of Machine Learning Research, 2015.Markdown
[Thon and Jaeger. "Links Between Multiplicity Automata, Observable Operator Models and Predictive State Representations -- a Unified Learning Framework." Journal of Machine Learning Research, 2015.](https://mlanthology.org/jmlr/2015/thon2015jmlr-links/)BibTeX
@article{thon2015jmlr-links,
title = {{Links Between Multiplicity Automata, Observable Operator Models and Predictive State Representations -- a Unified Learning Framework}},
author = {Thon, Michael and Jaeger, Herbert},
journal = {Journal of Machine Learning Research},
year = {2015},
pages = {103-147},
volume = {16},
url = {https://mlanthology.org/jmlr/2015/thon2015jmlr-links/}
}