Fetching the paper…
Reading the bibliography…
The Bernstein-von Mises theorem (BvM) gives conditions under which the posterior distribution of a parameter $\theta\in\Theta\subseteq\mathbb R^d$ based on $n$ independent samples is asymptotically normal.
Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
Stephen Portnoy · 1988
Earlier work this paper cites.
Real and Functional Analysis
Serge Lang · 1993
Earlier work this paper cites.
Bayesian data analysis
Andrew Gelman, John B Carlin, Hal S Stern, and Donald B Rubin · 1995
Earlier work this paper cites.
Asymptotic Statistics
A. W. van der Vaart · 1998
Earlier work this paper cites.
Wald lecture: On the Bernstein-von Mises theorem with infinite-dimensional parameters
David Freedman · 1999
Earlier work this paper cites.
Asymptotic normality of posterior distributions in high-dimensional linear models
Subhashis Ghosal · 1999
Earlier work this paper cites.
Asymptotic normality of posterior distributions for exponential families when the number of parameters tends to infinity
Subhashis Ghosal · 2000
Earlier work this paper cites.
On parameters of increasing dimensions
Xuming He and Qi-Man Shao · 2000
Earlier work this paper cites.
The geometry of logconcave functions and sampling algorithms
László Lovász and Santosh Vempala · 2007
Earlier work this paper cites.
Concentration Inequalities and Model Selection: Ecole d’Eté de Probabilités de Saint-Flour XXXIII-2003
Pascal Massart · 2007
Earlier work this paper cites.
A Bernstein-Von Mises Theorem for discrete probability distributions
S. Boucheron and E. Gassiat · 2009
Earlier work this paper cites.
Quantitative estimates of the convergence of the empirical covariance matrix in log-concave ensembles
Radoslaw Adamczak, Alexander Litvak, Alain Pajor, and Nicole Tomczak-Jaegermann · 2010
Earlier work this paper cites.
Bernstein–von Mises theorems for Gaussian regression with increasing number of regressors
Dominique Bontemps · 2011
Earlier work this paper cites.
The Bernstein-von-Mises theorem under misspecification
Bas JK Kleijn and Aad W van der Vaart · 2012
Cited alongside, same era.
Parametric estimation. Finite sample theory
Vladimir Spokoiny · 2012
Cited alongside, same era.
The best rank-1 approximation of a symmetric tensor and related spherical optimization problems
Xinzhen Zhang, Chen Ling, and Liqun Qi · 2012
Cited alongside, same era.
Nonparametric bernstein–von Mises theorems in Gaussian white noise
Ismaël Castillo and Richard Nickl · 2013
Cited alongside, same era.
Bernstein-von Mises theorem for growing parameter dimension
Vladimir Spokoiny · 2013
Cited alongside, same era.
Analysis and geometry of Markov diffusion operators
Dominique Bakry, Ivan Gentil, Michel Ledoux, et al · 2014
The total variation distance between high-dimensional gaussians with the same mean
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
Later among the works it cites.
A deterministic and computable Bernstein-von Mises theorem
Guillaume P Dehaene · 2019
Later among the works it cites.
A modern maximum-likelihood theory for high-dimensional logistic regression
Pragya Sur · 2019
Later among the works it cites.
Laplace and saddlepoint approximations in high dimensions
Yanbo Tang and Nancy Reid · 2021
Later among the works it cites.
Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems
Tapio Helin and Remo Kretschmann · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Information and exponential families: in statistical theory
Ole Barndorff-Nielsen · 2014
Cited alongside, same era.
Posterior inference in curved exponential families under increasing dimensions
Alexandre Belloni and Victor Chernozhukov · 2014
Cited alongside, same era.
Log-concavity and strong log-concavity: a review
Adrien Saumard and Jon A Wellner · 2014
Cited alongside, same era.
Bayesian linear regression with sparse priors
Ismaël Castillo, Johannes Schmidt-Hieber, and Aad van der Vaart · 2015
Cited alongside, same era.
Finite sample Bernstein–von Mises theorem for semiparametric problems
Maxim Panov and Vladimir Spokoiny · 2015
Cited alongside, same era.
Laplace approximation in high-dimensional Bayesian regression
Rina Foygel Barber, Mathias Drton, and Kean Ming Tan · 2016
Cited alongside, same era.
Mikolaj J Kasprzak, Ryan Giordano, and Tamara Broderick · 2022
Later among the works it cites.
On polynomial-time computation of high-dimensional posterior measures by langevin-type algorithms
Richard Nickl and Sven Wang · 2022
Later among the works it cites.
Finite samples inference and critical dimension for stochastically linear models
Vladimir Spokoiny · 2022
Later among the works it cites.
The Laplace approximation accuracy in high dimensions: a refined analysis and new skew adjustment
Anya Katsevich · 2023
Closest in time.
Dimension free nonasymptotic bounds on the accuracy of high-dimensional laplace approximation
Vladimir Spokoiny · 2023
Closest in time.
Inexact laplace approximation and the use of posterior mean in bayesian inference
Vladimir Spokoiny · 2023
Closest in time.
The laplace asymptotic expansion in high dimensions
Anya Katsevich · 2024
Closest in time.