A History of the Central Limit Theorem: From Classical to Modern Probability Theory

294 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks (2019, background): “In this case, generalized central limit theorem (GCLT) says that the sum of such variables will converge to a distribution called the α -stable distribution instead as the number of summands grows (see e.g. [15].”
Mod-ϕ Convergence (2016, result): “We refer to [Fis11] for an historical account of this classical result.”
L\'evy walks (2014, background): “nsiderations by Laplace and Poisson at the end of the 18th century to the stage of rigorous analysis by Markov, Chebyshev, Lyapunov, Feller, Levy and others in the beginning of the 20th century, see Fischer (2010) for historical overview. Remarkably, the CLT can be cast into the dynamical problem of”