Performance Guarantees for Information Theoretic Active Inference
Abstract
In many estimation problems, the measurement process can be actively controlled to alter the information received. The control choices made in turn determine the performance that is possible in the underlying inference task. In this paper, we discuss performance guarantees for heuristic algorithms for adaptive measurement selection in sequential estimation problems, where the inference criterion is mutual information. We also demonstrate the performance of our tighter online computable performance guarantees through computational simulations.
Cite
Text
Williams et al. "Performance Guarantees for Information Theoretic Active Inference." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Williams et al. "Performance Guarantees for Information Theoretic Active Inference." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/williams2007aistats-performance/)BibTeX
@inproceedings{williams2007aistats-performance,
title = {{Performance Guarantees for Information Theoretic Active Inference}},
author = {Williams, Jason L. and Iii, John W. Fisher and Willsky, Alan S.},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {620-627},
volume = {2},
url = {https://mlanthology.org/aistats/2007/williams2007aistats-performance/}
}