3 citations · 5 across the 6 of their papers we have counts for
6 papers
An Order of Magnitude Time Complexity Reduction for Gaussian Graphical Model Posterior Sampling Using a Reverse Telescoping Block Decomposition
Zejin Gao, Ksheera Sagar, Anindya Bhadra
We consider the problem of fully Bayesian posterior estimation and uncertainty quantification in undirected Gaussian graphical models via Markov chain Monte Carlo (MCMC) under rece…
Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation
Ksheera Sagar, Jyotishka Datta, Sayantan Banerjee +1
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally…
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
Ksheera Sagar, Yang Ni, Veerabhadran Baladandayuthapani +1
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend…
A Laplace Mixture Representation of the Horseshoe and Some Implications
Ksheera Sagar, Anindya Bhadra
The horseshoe prior, defined as a half Cauchy scale mixture of normal, provides a state of the art approach to Bayesian sparse signal recovery. We provide a new representation of t…
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix
Anindya Bhadra, Ksheera Sagar, David Rowe +2
Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tunin…
Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual
Ksheera Sagar, Sayantan Banerjee, Jyotishka Datta +1
Precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is diff…