A Method of Computing Generalized Bayesian Probability Values for Expert Systems
Proceedings of the Eighth International Joint Conference on Artificial Intelligence. Expert Systems. Pages 198-201.
Abstract
This paper presents a new method for calculating the condi t ional probability of any multi-valued predicate given particular inform ation about the individual case. This calculation is based on the principle of Maximum Entropy (ME), sometimes called the principle of least information, and gives the most unbiased probability estimate given the available evidence. Previous methods for computing maximum entropy values shows that they are either very restrictive in the probabilistic inform at ion (constraints) they can use or combinatorially explosive. Th e computational complexity of the new procedure depends on the inter-connectedness of the constraints, but in practical cases it is small. In addition, the maximum entropy met hod can give a measure of how accurately a calculated con· ditional probability is known.
@inproceedings{ijcai1983p47,
title = {A Method of Computing Generalized Bayesian Probability Values for Expert Systems},
author = {Cheeseman, Peter C.},
booktitle = {Proceedings of the Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-83}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Alan Bundy},
pages = {198--201},
year = {1983},
note = {Expert Systems},
url = {https://ijcai.org/proceedings/1983/47},
}
TY - CPAPER TI - A Method of Computing Generalized Bayesian Probability Values for Expert Systems AU - Cheeseman, Peter C. T2 - Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI-83 PB - International Joint Conferences on Artificial Intelligence PY - 1983 SP - 198 EP - 201 UR - https://ijcai.org/proceedings/1983/47 ER -Copied
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