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Stein's method (Stein, 1973; 1981) is a powerful tool for statistical applications and has significantly impacted machine learning.
A useful theorem for nonlinear devices having Gaussian inputs
Price, R · 1958
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Transformations des signaux aléatoires a travers les systemes non linéaires sans mémoire
Bonnet, G · 1964
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Estimation of the Mean of a Multivariate Normal Distribution
Stein, C · 1973
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A natural identity for exponential families with applications in multiparameter estimation
Hudson, H. M. et al · 1978
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Estimation of the mean of a multivariate normal distribution
Stein, C. M · 1981
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Fundamentals of statistical exponential families: with applications in statistical decision theory
Brown, L. D · 1986
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Siegel’s formula via Stein’s identities
Liu, J. S · 1994
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A multivariate version of stein’s identity with applications to moment calculations and estimation of conditionally specified distributions
Arnold, B. C., Castillo, E., and Sarabia, J. M · 2001
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The heat equation and Stein’s identity: Connections, applications
Brown, L., DasGupta, A., Haff, L. R., and Strawderman, W. E · 2006
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On the generalization of Stein’s Lemma for elliptical class of distributions
Landsman, Z · 2006
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Extensions of Stein’s lemma for the skew-normal distribution
Adcock, C · 2007
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Stein’s Lemma for elliptical random vectors
Landsman, Z. and Nešlehová, J · 2008
Cited alongside, same era.
On Stein’s identity and its applications
Kattumannil, S. K · 2009
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The variational Gaussian approximation revisited
Opper, M. and Archambeau, C · 2009
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Real analysis
Royen, H. and Fitzpatrick, P · 2010
Cited alongside, same era.
On the multivariate extended skew-normal, normal-exponential, and normal-gamma distributions
Adcock, C. and Shutes, K · 2012
Cited alongside, same era.
On the equivalence between Stein and de Bruijn identities
Park, S., Serpedin, E., and Qaraqe, K · 2012
Cited alongside, same era.
Fast second order stochastic backpropagation for variational inference
Fan, K., Wang, Z., Beck, J., Kwok, J., and Heller, K. A · 2015
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On generalized moment identity and its applications: a unified approach
Kattumannil, S. K. and Dewan, I · 2016
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Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models
Khan, M. and Lin, W · 2017
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A first course in Sobolev spaces , volume 181
Leoni, G · 2017
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Implicit Reparameterization Gradients
Figurnov, M., Mohamed, S., and Mnih, A · 2018
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Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Khan, M. E., Nielsen, D., Tangkaratt, V., Lin, W., Gal, Y., and Srivastava, A · 2018
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Kingma, D. P. and Welling, M · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, T. and Knowles, D · 2013
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Newton-Stein method: a second order method for GLMs via Stein’s Lemma
Erdogdu, M. A · 2015
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Lecture Notes: integration by parts
Border, K. C · 2019
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Lecture Notes: Stein’s method
Chatterjee, S · 2019
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Lecture Notes: Honors Real Variable II
Jia, R.-Q · 2019
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Fast and simple natural-gradient variational inference with mixture of exponential-family approximations
Lin, W., Khan, M. E., and Schmidt, M · 2019
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