Constraints as Data: A New Perspective on Inferring Probabilities
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. Pages 755-763.
Abstract
We present a new approach to inferring a probability distribution which is incompletely specified by a number of linear constraints. We argue that the currently most popular approach of entropy maximization depends on a “constraints as knowledge” interpretation of the constraints, and that a different “constraints as data” perspective leads to a completely different type of inference procedures by statistical methods. With statistical methods some of the counterintuitive results of entropy maximization can be avoided, and inconsistent sets of constraints can be handled just like consistent ones. A particular statistical inference method is developed and shown to have a nice robustness property.
@inproceedings{ijcai2001p103,
title = {Constraints as Data: A New Perspective on Inferring Probabilities},
author = {Jaeger, Manfred},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {755--763},
year = {2001},
url = {https://ijcai.org/proceedings/2001/103},
}
TY - CPAPER TI - Constraints as Data: A New Perspective on Inferring Probabilities AU - Jaeger, Manfred T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 755 EP - 763 UR - https://ijcai.org/proceedings/2001/103 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
