118 citations · 689 across the 53 of their papers we have counts for
11 papers · 1 filter
Pseudo-likelihood Estimators for Graphical Models: Existence and Uniqueness
Benjamin Roycraft, Bala Rajaratnam
Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high dimensional applications. A part of their wide appeal stems from the signif…
A unified framework for correlation mining in ultra-high dimension
Yun Wei, Bala Rajaratnam, Alfred O. Hero
Many applications benefit from theory relevant to the identification of variables having large correlations or partial correlations in high dimension. Recently there has been progr…
MCMC-Based Inference in the Era of Big Data: A Fundamental Analysis of the Convergence Complexity of High-Dimensional Chains
Bala Rajaratnam, Doug Sparks
Markov chain Monte Carlo (MCMC) lies at the core of modern Bayesian methodology, much of which would be impossible without it. Thus, the convergence properties of MCMCs have receiv…
Foundational principles for large scale inference: Illustrations through correlation mining
Alfred O. Hero, Bala Rajaratnam
When can reliable inference be drawn in the "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, with i…
The Letac-Massam conjecture and existence of high dimensional Bayes estimators for Graphical Models
Emanuel Ben-David, Bala Rajaratnam
In recent years, a variety of useful extensions of the Wishart have been proposed in the literature for the purposes of studying Markov random fields/graphical models. In particula…
Hub discovery in partial correlation graphical models
Alfred Hero, Bala Rajaratnam
This paper treats the problem of screening a p-variate sample for strongly and multiply connected vertices in the partial correlation graph associated with the the partial correlat…