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Estimating mutual information (MI) from samples is a fundamental problem in statistics, machine learning, and data analysis.
A mathematical theory of communication
C.E. Shannon · 1948
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Convergence conditions for ascent methods
Philip Wolfe · 1969
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Incorporating support constraints into nonparametric estimators of densities
Eugene F Schuster · 1985
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Density estimation for statistics and data analysis , volume 26
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Transformations to reduce boundary bias in kernel density estimation
James Stephen Marron and David Ruppert · 1994
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Estimation of mutual information using kernel density estimators
Young-Il Moon, Balaji Rajagopalan, and Upmanu Lall · 1995
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On pseudodata methods for removing boundary effects in kernel density estimation
Ann Cowling and Peter Hall · 1996
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NL Hjort and MC Jones · 1996
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Local likelihood density estimation
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Joseph E Yukich and Joseph Yukich · 1998
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On nonparametric density estimation at the boundary*
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A. Kraskov, H. Stögbauer, and P. Grassberger · 2004
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Detecting novel associations in large data sets
David N Reshef, Yakir A Reshef, Hilary K Finucane, Sharon R Grossman, Gilean McVean, Peter J Turnbaugh, Eric S Lander, Michael Mitzenmacher, and Pardis C Sabeti · 2011
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Ensemble estimators for multivariate entropy estimation
K. Sricharan, D. Wei, and A.O. Hero · 2013
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Equitability, mutual information, and the maximal information coefficient
J. Kinney and G. Atwal · 2014
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Ensemble estimation of multivariate f-divergence
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Generalized exponential concentration inequality for renyi divergence estimation
Shashank Singh and Barnabas Poczos · 2014
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Efficient estimation of mutual information for strongly dependent variables
Shuyang Gao, Greg Ver Steeg, and Aram Galstyan · 2015
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