4 papers
Parametric Mean-Field empirical Bayes in high-dimensional linear regression
Seunghyun Lee, Nabarun Deb
In this paper, we consider the problem of parametric empirical Bayes estimation of an i.i.d. prior in high-dimensional Bayesian linear regression, with random design. We obtain the…
Inference on Gaussian mixture models with dependent labels
Seunghyun Lee, Rajarshi Mukherjee, Sumit Mukherjee
Gaussian mixture models are widely used to model data generated from multiple latent sources. Despite its popularity, most theoretical research assumes that the labels are either i…
CLT in high-dimensional Bayesian linear regression with low SNR
Seunghyun Lee, Nabarun Deb, Sumit Mukherjee
We study central limit theorems for linear statistics in high-dimensional Bayesian linear regression with product priors. Unlike the existing literature where the focus is on poste…
Fluctuations in random field Ising models
Seunghyun Lee, Nabarun Deb, Sumit Mukherjee
This paper establishes a CLT for linear statistics of the form with quantitative Berry-Esseen bounds, where is an observati…