This paper presents a new algorithm for generating random inverse-Wishart matrices that directly generates the Cholesky factor of the matrix without computing the factorization. Whenever parameterized in terms of a precision matrix $\Omega=\Sigma^{-1}$, or its Cholesky factor, instead of a covariance matrix $\Sigma$, the new algorithm is more efficient than the current standard algorithm.

翻译：本文提出了一种生成随机逆Wishart矩阵的新算法，该算法直接生成矩阵的Cholesky因子，无需计算其分解。当以精度矩阵$\Omega=\Sigma^{-1}$（或其Cholesky因子）而非协方差矩阵$\Sigma$进行参数化时，新算法比现行标准算法更高效。