52 citations · 57 across the 5 of their papers we have counts for
5 papers
From Random Walks to Random Leaps: Generalizing Classic Markov Chains for Big Data Applications
Bala Rajaratnam, Narut Sereewattanawoot, Doug Sparks +1
Simple random walks are a basic staple of the foundation of probability theory and form the building block of many useful and complex stochastic processes. In this paper we study a…
Scalable Bayesian shrinkage and uncertainty quantification in high-dimensional regression
Bala Rajaratnam, Doug Sparks, Kshitij Khare +1
Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncert…
Scalable Bayesian shrinkage and uncertainty quantification for high-dimensional regression
Bala Rajaratnam, Doug Sparks, Kshitij Khare +1
Bayesian shrinkage methods have generated a lot of recent interest as tools for high-dimensional regression and model selection. These methods naturally facilitate tractable uncert…
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…
Lasso Regression: Estimation and Shrinkage via Limit of Gibbs Sampling
Bala Rajaratnam, Steven Roberts, Doug Sparks +1
The application of the lasso is espoused in high-dimensional settings where only a small number of the regression coefficients are believed to be nonzero. Moreover, statistical pro…