68 citations · 353 across the 57 of their papers we have counts for
7 papers · 1 filter
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…
Grand Challenges in Bayesian Computation
Anirban Bhattacharya, Antonio Linero, Chris. J. Oates
This article appeared in the September 2024 issue (Vol. 31, No. 3) of the Bulletin of the International Society for Bayesian Analysis (ISBA).
A Hybrid Approximation to the Marginal Likelihood
Eric Chuu, Debdeep Pati, Anirban Bhattacharya
Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predom…
Approximate Laplace approximations for scalable model selection
David Rossell, Oriol Abril, Anirban Bhattacharya
We propose the approximate Laplace approximation (ALA) to evaluate integrated likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a popular too…
The Soft Multivariate Truncated Normal Distribution with Applications to Bayesian Constrained Estimation
Allyson Souris, Anirban Bhattacharya, Debdeep Pati
We propose a new distribution, called the soft tMVN distribution, which provides a smooth approximation to the truncated multivariate normal (tMVN) distribution with linear constra…
Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior
James E. Johndrow, Paulo Orenstein, Anirban Bhattacharya
The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse.…
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are as…