Copyright (C) 2021-2022
Benjamin Paaßen
German Research Center for Artificial Intelligence
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.
This is the accompanying source code for the EDM 2022 poster 'Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood'. If you use this implementation in academic work, please cite the paper
- Paaßen, B., Göpfert, C., & Pinkwart, N. (2022). Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. In: Cristea, A., Brown, C., Mitrovic, T., & Bosch, N. (Eds.). Proceedings of the 15th International Conference on Educational Datamining (EDM 2022). accepted.
@inproceedings{Paassen2022EDM,
author = {Paaßen, Benjamin and Göpfert, Christina and Pinkwart, Niels},
title = {Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood},
booktitle = {{Proceedings of the 14th International Conference on Educational Data Mining (EDM 2022)}},
date = {2022-07-24},
year = {2022},
venue = {Durham, UK},
editor = {Cristea, Alexandra I. and Brown, Chris and Mitrovic, Tanja and Bosch, Nigel},
note = {accepted}
}The reference implementation for all confidence bound methods can be found in ability_bounds.py. The experimental source code in the notebook synthetic_experiments.ipynb.
The source code in this repository depends on numpy and scipy.