Uncertainty in Artificial Intelligence
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On Modeling Profiles instead of Values
Alon Orlitsky, Narayana Santhanam, Krishnamurthy Viswanathan, Junan Zhang
Abstract:
We consider the problem of estimating the distribution underlying an observed sample of data. Instead of maximum likelihood, which maximizes the probability of the ob served values, we propose a different estimate, the high-profile distribution, which maximizes the probability of the observed profile the number of symbols appearing any given number of times. We determine the high-profile distribution of several data samples, establish some of its general properties, and show that when the number of distinct symbols observed is small compared to the data size, the high-profile and maximum-likelihood distributions are roughly the same, but when the number of symbols is large, the distributions differ, and high-profile better explains the data.
Keywords: null
Pages: 426-435
PS Link:
PDF Link: /papers/04/p426-orlitsky.pdf
BibTex:
@INPROCEEDINGS{Orlitsky04,
AUTHOR = "Alon Orlitsky and Narayana Santhanam and Krishnamurthy Viswanathan and Junan Zhang",
TITLE = "On Modeling Profiles instead of Values",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "426--435"
}


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