We propose a Cholesky factor parameterization of correlation matrices that facilitates a priori restrictions on the correlation matrix. It is a smooth and differentiable transform that allows additional boundary constraints on the correlation values. Our particular motivation is random sampling under positivity constraints on the space of correlation matrices using MCMC methods.

翻译：我们提出了一种相关矩阵的Cholesky因子参数化方法，该方法便于对相关矩阵实施先验约束。这是一种光滑可微的变换，允许对相关值施加额外的边界约束。我们特别的研究动机是使用MCMC方法在相关矩阵空间的正性约束下进行随机采样。