A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference
用于稀疏贝叶斯推断的快速异步 MCMC 采样器
Abstract We propose a very fast approximate Markov chain Monte Carlo sampling framework that is applicable to a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), where n is the sample size, s is the underlying sparsity of the model, and J is the size of a randomly selected subset of regressors. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin…