10 papers
Adaptive Resolution for Finite-Rank Gaussian Processes
Jaehoan Kim, Anirban Bhattacharya, Debdeep Pati
Finite-rank approximations are widely used to scale Gaussian process (GP) regression, but their posterior behavior can differ from that of the corresponding parent GP prior. We stu…
Robust Simulation Based Inference Through Robust Optimal Transport
Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya +1
When a statistical model lacks analytically tractable likelihoods, parametric statistical inference based on data generated from an unknown underlying distrib…
Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
Somjit Roy, Prateek Jaiswal, Anirban Bhattacharya +2
We study Gaussian Process Thompson Sampling (GP-TS) for sequential decision-making over compact, continuous action spaces and provide a frequentist regret analysis based on fractio…
Robust Bayesian Inference on Riemannian Submanifold
Rong Tang, Anirban Bhattacharya, Debdeep Pati +1
Manifold-valued parameters routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian a…
On Quantification of Borrowing of Information in Hierarchical Bayesian Models
Prasenjit Ghosh, Anirban Bhattacharya, Debdeep Pati
In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing…
A note on simulation methods for the Dirichlet-Laplace prior
Luis Gruber, Gregor Kastner, Anirban Bhattacharya +3
Bhattacharya et al. (2015, Journal of the American Statistical Association 110(512): 1479-1490) introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chai…