6 papers
Statistical inference for boundary constrained parameters
Jaeseon Lee, Anirban Bhattacharya, Suhasini Subba Rao
Estimation and inference under boundary constraints arises in many areas of statistics, but the available asymptotic theory remains limited and fragmented relative to the classical…
MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni
We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors. Motiva…
Consistent Bayesian causal discovery for structural equation models with equal error variances
Anamitra Chaudhuri, Yang Ni, Anirban Bhattacharya
We consider the problem of recovering the true causal structure among a set of variables, generated by a linear acyclic structural equation model (SEM) with the error terms being i…
Conditionally specified graphical modeling of stationary multivariate time series
Anirban Bhattacharya, Jan Johannes, Suhasini Subba Rao
Graphical models are ubiquitous for summarizing conditional relations in multivariate data. In many applications involving multivariate time series, it is of interest to learn an i…
Acceleration via silver step-size on Riemannian manifolds with applications to Wasserstein space
Jiyoung Park, Abhishek Roy, Jonathan W. Siegel +1
There is extensive literature on accelerating first-order optimization methods in a Euclidean setting. Under which conditions such acceleration is feasible in Riemannian optimizati…
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples
Leo L. Duan, Anirban Bhattacharya
It has become increasingly easy nowadays to collect approximate posterior samples via fast algorithms such as variational Bayes, but concerns exist about the estimation accuracy. I…