Results 11 to 20 of about 6,796 (225)
Spin glasses and Stein's method [PDF]
Probability Theory and Related Fields, 2009 We introduce some applications of Stein's method in the high temperature analysis of spin glasses. Stein's method allows the direct analysis of the Gibbs measure without having to create a cavity.
Chatterjee, Sourav
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The Stein-Dirichlet-Malliavin method [PDF]
ESAIM: Proceedings and Surveys, 2015 The Stein’s method is a popular method used to derive upper-bounds of distances between probability distributions. It can be viewed, in certain of its formulations, as an avatar of the semi-group or of the smart-path method used ...
Decreusefond L.
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Discretized normal approximation by Stein's method
Bernoulli, 2014 We prove a general theorem to bound the total variation distance between the distribution of an integer valued random variable of interest and an appropriate discretized normal distribution.
Fang, Xiao
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Multidimensional Stein's method for Gamma approximation [PDF]
Latin American Journal of Probability and Mathematical Statistics, 2023 Let F ($ν$) be the centered Gamma law with parameter $ν$ > 0 and let us denote by P Y the probability distribution of a random vector Y. We develop a multidimensional variant of the Stein's method for Gamma approximation that allows to obtain bounds for the second Wasserstein distance between the probability distribution of an arbitrary random ...
Ciprian A. Tudor, Jérémy Zurcher
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On Stein factors in Stein's method for normal approximation [PDF]
Statistics & Probability LettersBuilding on the rather large literature concerning the regularity of the solution of the standard normal Stein equation, we provide a complete description of the best possible uniform bounds for the derivatives of the solution of the standard normal Stein equation when the test functions belong to the class of real-valued functions whose $k$-th order ...
Robert E. Gaunt
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Stein's method, smoothing and functional approximation [PDF]
Electronic Journal of Probability, 2021 Stein's method for Gaussian process approximation can be used to bound the differences between the expectations of smooth functionals $h$ of a càdlàg random process $X$ of interest and the expectations of the same functionals of a well understood target random process $Z$ with continuous paths.
A. D. Barbour +2 more
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Bernoulli, 2022 This paper proposes and studies a numerical method for approximation of posterior expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size bounds on the approximation error are established for posterior distributions supported on a compact Riemannian manifold, and we relate these to a kernel Stein discrepancy (KSD ...
Barp A +3 more
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Stein’s method and Narayana numbers [PDF]
Statistics & Probability Letters, 2020 Narayana numbers appear in many places in combinatorics and probability, and it is known that they are asymptotically normal. Using Stein's method of exchangeable pairs, we provide an error of approximation in total variation to a symmetric binomial distribution of order~$n^{-1}$, which also implies a Kolmogorov bound of order~$n^{-1/2}$ for the normal
Jason Fulman, Adrian Röllin
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Electronic Journal of Probability, 2020 Applying an inductive technique for Stein and zero bias couplings yields Berry-Esseen theorems for normal approximation for two new examples. The conditions of the main results do not require that the couplings be bounded. Our two applications, one to the Erd s-R nyi, random graph with a fixed number of edges, and one to Jack measure on tableaux ...
Chen, L.H.Y., Goldstein, L., Röllin, A.
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Stein's method of exchangeable pairs in multivariate functional approximations [PDF]
, 2021 In this paper we develop a framework for multivariate functional approximation by a suitable Gaussian process via an exchangeable pairs coupling that satisfies a suitable approximate linear regression property, thereby building on work by Barbour (1990 ...
Döbler, Christian +1 more
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