Constructing Small Sample Spaces Satisfying Given Constraints (1994)by D. Koller and N. Megiddo
Abstract:
The subject of this paper is finding small sample spaces for joint distributions of n discrete random variables. Such distributions are often only required to obey a certain limited set of constraints of the form Pr(Event) = pi. We show that the problem of deciding whether there exists any distribution satisfying a given set of constraints is NP-hard. However, if the constraints are consistent, then there exists a distribution satisfying them which is supported by a ``small'' sample space (one whose cardinality is equal to the number of constraints). For the important case of independence constraints, where the constraints have a certain form and are consistent with a joint distribution of independent random variables, a small sample space can be constructed in polynomial time. This last result can be used to derandomize algorithms; we demonstrate this by an application to the problem of finding large independent sets in sparse hypergraphs.
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D. Koller and N. Megiddo (1994). "Constructing Small Sample Spaces Satisfying Given Constraints." Siam Journal on Discrete Mathematics, 7(2), 260-274.
Full version of paper in STOC '93.
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Bibtex citation
@article{Koller+Megiddo:94,
author = "D. Koller and N. Megiddo",
title = "Constructing Small Sample Spaces Satisfying Given
Constraints",
journal = "Siam Journal on Discrete Mathematics",
volume = "7",
number = "2",
pages = "260--274",
year = "1994",
note = "Full version of paper in STOC '93",
}
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