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Double-Parallel Monte Carlo for Bayesian Analysis of Big Data.
Statistics and Computing ( IF 1.6 ) Pub Date : 2017-11-27 , DOI: 10.1007/s11222-017-9791-1
Jingnan Xue 1 , Faming Liang 2
Affiliation  

This paper proposes a simple, practical, and efficient MCMC algorithm for Bayesian analysis of big data. The proposed algorithm suggests to divide the big dataset into some smaller subsets and provides a simple method to aggregate the subset posteriors to approximate the full data posterior. To further speed up computation, the proposed algorithm employs the population stochastic approximation Monte Carlo algorithm, a parallel MCMC algorithm, to simulate from each subset posterior. Since this algorithm consists of two levels of parallel, data parallel and simulation parallel, it is coined as “Double-Parallel Monte Carlo.” The validity of the proposed algorithm is justified mathematically and numerically.

中文翻译:

贝叶斯大数据贝叶斯分析的双重并行蒙特卡洛方法。

本文提出了一种简单,实用,高效的MCMC算法用于大数据的贝叶斯分析。提出的算法建议将大数据集划分为一些较小的子集,并提供一种简单的方法来聚合子集后代以近似整个数据后验。为了进一步加快计算速度,该算法采用了种群随机逼近蒙特卡罗算法(一种并行的MCMC算法)来对每个子集后验进行仿真。由于该算法由并行,数据并行和模拟并行两个级别组成,因此被称为“双并行蒙特卡洛”。该算法的有效性在数学和数值上得到了证明。
更新日期:2017-11-27
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