In recent years, big datasets are often split into several subsets due to the storage requirements. We propose a parallel group Bayesian method for statistical inference in sparse big data. This method improves the existing methods in two aspects: the total datasets are also split into a data subset sequence and the parameter vector is divided into several sub-vectors. Besides, we add a weight sequence to optimize the sub-estimators when each of them has a different covariance matrix. We obtain several theoretical properties of the estimator. The results of numerical simulations show that our method is consistent with the theoretical results and is more effective than classic Markov chain Monte Carlo methods.

中文翻译：

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使用群贝叶斯方法对大数据进行并行推理

近年来，由于存储要求，大数据集经常被分成几个子集。我们提出了一种并行群贝叶斯方法，用于稀疏大数据中的统计推断。该方法在两个方面对现有方法进行了改进：将总数据集也拆分为一个数据子集序列，并将参数向量拆分为多个子向量。此外，当每个子估计器具有不同的协方差矩阵时，我们添加了一个权重序列来优化子估计器。我们获得了估计器的几个理论属性。数值模拟结果表明，我们的方法与理论结果一致，比经典的马尔可夫链蒙特卡罗方法更有效。