Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a
Scalable MCMC Algorithm for the Horseshoe Prior

v1v2v3 (latest)

Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior

2 May 2017

Paulo Orenstein

A. Bhattacharya

ArXiv (abs)PDF HTML

Papers citing "Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior"

6 / 6 papers shown

Title
Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates A. Nishimura M. Suchard 110 9 0 06 Nov 2019
Estimating Convergence of Markov chains with L-Lag Couplings N. Biswas Pierre E. Jacob Paul Vanetti 58 49 0 23 May 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian L. Trippe Jonathan H. Huggins Raj Agrawal Tamara Broderick BDL 73 9 0 17 May 2019
Horseshoe Regularization for Machine Learning in Complex and Deep Models A. Bhadra J. Datta Yunfan Li Nicholas G. Polson 87 15 0 24 Apr 2019
Prior-preconditioned conjugate gradient method for accelerated Gibbs sampling in "large $n$ & large $p$ " Bayesian sparse regression A. Nishimura M. Suchard 66 20 0 29 Oct 2018
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler Alexander Terenin Måns Magnusson Leif Jonsson D. Draper 72 18 0 12 Apr 2017