Probability estimation in face of irrelevant information (1991)by A.J. Grove and D. Koller
Abstract:
In this paper, we consider one aspect of the problem of applying decision theory to the design of agents that learn how to make decisions under uncertainty. This aspect concerns how an agent can estimate probabilities for the possible states of the world, given that it only makes limited observations before committing to a decision. We show that the naive application of statistical tools can be improved upon if the agent can determine which of his observations are truly relevant to the estimation problem at hand. We give a framework in which such determinations can be made, and define an estimation procedure to use them. Our framework also suggests several extensions, which show how additional knowledge can be used to improve the estimation procedure still further.
Download Information
A.J. Grove and D. Koller (1991). "Probability estimation in face of irrelevant information." Proceedings of the 7th Annual Conference on Uncertainty in AI (UAI) (pp. 127134).


Bibtex citation
@inproceedings{Grove+Koller:UAI91,
title = {Probability estimation in face of irrelevant information},
author = {A.J. Grove and D. Koller},
booktitle = {Proceedings of the 7th Annual Conference on
Uncertainty in AI (UAI)},
address = {Los Angeles, California},
month = {July},
year = 1991,
pages = {127134},
}
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