155 citations · 955 across the 56 of their papers we have counts for
9 papers · 1 filter
A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models
Gautam Sabnis, Debdeep Pati, Barbara Engelhardt +1
We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian fact…
Parallel local approximation MCMC for expensive models
Patrick Conrad, Andrew Davis, Youssef Marzouk +2
Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model…
Parallel Markov Chain Monte Carlo via Spectral Clustering
Guillaume W. Basse, Natesh S. Pillai, Aaron Smith
As it has become common to use many computer cores in routine applications, finding good ways to parallelize popular algorithms has become increasingly important. In this paper, we…
On the Mixing Time of Kac's Walk and Other High-Dimensional Gibbs Samplers with Constraints
Natesh S. Pillai, Aaron Smith
Determining the total variation mixing time of Kac's random walk on the special orthogonal group has been a long-standing open problem. In this paper, we construct…
More Powerful Multiple Testing in Randomized Experiments with Non-Compliance
Joseph J. Lee, Laura Forastiere, Luke Miratrix +1
Two common concerns raised in analyses of randomized experiments are (i) appropriately handling issues of non-compliance, and (ii) appropriately adjusting for multiple tests (e.g.,…
Sub-optimality of some continuous shrinkage priors
Anirban Bhattacharya, David B. Dunson, Debdeep Pati +1
Two-component mixture priors provide a traditional way to induce sparsity in high-dimensional Bayes models. However, several aspects of such a prior, including computational comple…
MCMC for Imbalanced Categorical Data
James E. Johndrow, Aaron Smith, Natesh Pillai +1
Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing informatio…
Ratios and Cauchy Distribution
Natesh S. Pillai
It is well known that the ratio of two independent standard Gaussian random variables follows a Cauchy distribution. Any convex combination of independent standard Cauchy random va…
Weak separation in mixture models and implications for principal stratification
Avi Feller, Evan Greif, Nhat Ho +2
Principal stratification is a widely used framework for addressing post-randomization complications. After using principal stratification to define causal effects of interest, rese…