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Score matching is an alternative to maximum likelihood (ML) for estimating a probability distribution parametrized up to a constant of proportionality.
Sur les lois de probabilitéa estimation exhaustive
Georges Darmois · 1935
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On distributions admitting a sufficient statistic
Bernard Osgood Koopman · 1936
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Sufficient statistics and intrinsic accuracy
Edwin James George Pitman · 1936
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The complexity of theorem-proving procedures
Stephen A Cook · 1971
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Efficiency of pseudolikelihood estimation for simple gaussian fields
Julian Besag · 1977
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Np is as easy as detecting unique solutions
Leslie G Valiant and Vijay V Vazirani · 1985
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Fundamentals of statistical exponential families: with applications in statistical decision theory
Lawrence D Brown · 1986
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Hypergeometric summation
Wolfram Koepf · 1998
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Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Some extensions of score matching
Aapo Hyvärinen · 2007
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Computational complexity: a modern approach
Sanjeev Arora and Boaz Barak · 2009
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Structure learning of antiferromagnetic ising models
Guy Bresler, David Gamarnik, and Devavrat Shah · 2014
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Efficiently learning ising models on arbitrary graphs
Guy Bresler · 2015
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Linear estimating equations for exponential families with application to gaussian linear concentration models
Computational implications of reducing data to sufficient statistics
Andrea Montanari · 2015
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Interaction screening: Efficient and sample-optimal learning of ising models
Marc Vuffray, Sidhant Misra, Andrey Lokhov, and Michael Chertkov · 2016
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Minimum stein discrepancy estimators
Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey · 2019
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Learning ising models from one or multiple samples
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, and Anthimos Vardis Kandiros · 2021
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Learning continuous exponential families beyond gaussian
Christopher X Ren, Sidhant Misra, Marc Vuffray, and Andrey Y Lokhov · 2021
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Peter GM Forbes and Steffen Lauritzen · 2015
Cited alongside, same era.
A computationally efficient method for learning exponential family distributions
Abhin Shah, Devavrat Shah, and Gregory Wornell · 2021
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Statistical efficiency of score matching: The view from isoperimetry
Frederic Koehler, Alexander Heckett, and Andrej Risteski · 2022
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