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Anirban Bhattacharya
Researcher at Texas A&M University
Publications - 136
Citations - 2727
Anirban Bhattacharya is an academic researcher from Texas A&M University. The author has contributed to research in topics: Prior probability & Bayesian probability. The author has an hindex of 23, co-authored 104 publications receiving 2146 citations. Previous affiliations of Anirban Bhattacharya include Florida State University & University of Nottingham.
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Sparse Bayesian infinite factor models
TL;DR: This work proposes a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases, and develops an efficient Gibbs sampler that scales well as data dimensionality increases.
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Dirichlet–Laplace Priors for Optimal Shrinkage
TL;DR: In this paper, a new class of Dirichlet-laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation, is proposed, which can be expressed as global-local scale mixtures of Gaussians.
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Dirichlet-Laplace priors for optimal shrinkage
TL;DR: This article proposes a new class of Dirichlet–Laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation.
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Fast sampling with Gaussian scale mixture priors in high-dimensional regression
TL;DR: In this paper, the authors proposed an efficient way to sample from a class of structured multivariate Gaussian distributions using matrix multiplications and linear system solutions, which is applicable in settings where Gaussian scale mixture priors are used on highdimensional parameters.
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Posterior contraction in sparse Bayesian factor models for massive covariance matrices
TL;DR: In this article, a new class of continuous shrinkage priors is proposed for sparse Bayesian factor models with sparsity assumptions on the true covariance matrix, and the convergence rates of these priors are derived for high-dimensional covariance matrices.