标题： 使用幂逆Wishart先验的最大后验协方差估计 显示英文标题

标题： Maximum A Posteriori Covariance Estimation Using a Power Inverse Wishart Prior

摘要： 协方差矩阵的估计是许多多元统计方法中的初始步骤，例如主成分分析和因子分析，但在许多实际应用中，样本空间的维度相对于样本数量来说很大，通常的最大似然估计效果较差。 通常，通过建模或正则化可以得到改进。 从实际角度来看，这些方法通常计算量大，并依赖于近似。 作为快速替代方案，我们提出了一种基于新一类先验分布的易于计算的最大后验（MAP）估计器，该先验分布推广了逆Wishart先验，讨论了其性质，并在模拟数据和真实数据上展示了该估计器。 显示英文摘要

摘要： The estimation of the covariance matrix is an initial step in many multivariate statistical methods such as principal components analysis and factor analysis, but in many practical applications the dimensionality of the sample space is large compared to the number of samples, and the usual maximum likelihood estimate is poor. Typically, improvements are obtained by modelling or regularization. From a practical point of view, these methods are often computationally heavy and rely on approximations. As a fast substitute, we propose an easily calculable maximum a posteriori (MAP) estimator based on a new class of prior distributions generalizing the inverse Wishart prior, discuss its properties, and demonstrate the estimator on simulated and real data.