论文标题
与随机梯度MCMC应用相关序列的差异降低
Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC
论文作者
论文摘要
在本文中，我们为依赖序列的加性功能提出了一种新颖的实用差异方法。我们的方法结合了控制变体的使用与经验方差估计值的最小化结合。我们分析了所提出的方法的有限样本特性，并得出了过量渐近方差的有限时间界限至零。 We apply our methodology to Stochastic Gradient MCMC (SGMCMC) methods for Bayesian inference on large data sets and combine it with existing variance reduction methods for SGMCMC. We present empirical results carried out on a number of benchmark examples showing that our variance reduction method achieves significant improvement as compared to state-of-the-art methods at the expense of a moderate increase of computational overhead.
In this paper we propose a novel and practical variance reduction approach for additive functionals of dependent sequences. Our approach combines the use of control variates with the minimisation of an empirical variance estimate. We analyse finite sample properties of the proposed method and derive finite-time bounds of the excess asymptotic variance to zero. We apply our methodology to Stochastic Gradient MCMC (SGMCMC) methods for Bayesian inference on large data sets and combine it with existing variance reduction methods for SGMCMC. We present empirical results carried out on a number of benchmark examples showing that our variance reduction method achieves significant improvement as compared to state-of-the-art methods at the expense of a moderate increase of computational overhead.
