Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection
用于大规模贝叶斯变量选择的扩展随机梯度 MCMC
Summary Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be…