10.11 Cholesky Factors of Covariance Matrices

An \(M \times M\) covariance matrix \(\Sigma\) can be Cholesky factored to a lower triangular matrix \(L\) such that \(L\,L^{\top} = \Sigma\). If \(\Sigma\) is positive definite, then \(L\) will be \(M \times M\). If \(\Sigma\) is only positive semi-definite, then \(L\) will be \(M \times N\), with \(N < M\).

A matrix is a Cholesky factor for a covariance matrix if and only if it is lower triangular, the diagonal entries are positive, and \(M \geq N\). A matrix satisfying these conditions ensures that \(L \, L^{\top}\) is positive semi-definite if \(M > N\) and positive definite if \(M = N\).

A Cholesky factor of a covariance matrix requires \(N + \binom{N}{2} + (M - N)N\) unconstrained parameters.

Cholesky Factor of Covariance Matrix Transform

Stan’s Cholesky factor transform only requires the first step of the covariance matrix transform, namely log transforming the positive diagonal elements. Suppose \(x\) is an \(M \times N\) Cholesky factor. The above-diagonal entries are zero, the diagonal entries are positive, and the below-diagonal entries are unconstrained. The transform required is thus

\[ y_{m,n} = \left\{ \begin{array}{cl} 0 & \mbox{if } m < n, \\ \log x_{m,m} & \mbox{if } m = n, \mbox{ and} \\ x_{m,n} & \mbox{if } m > n. \end{array} \right. \]

Cholesky Factor of Covariance Matrix Inverse Transform

The inverse transform need only invert the logarithm with an exponentiation. If \(y\) is the unconstrained matrix representation, then the elements of the constrained matrix \(x\) is defined by

\[ x_{m,n} = \left\{ \begin{array}{cl} 0 & \mbox{if } m < n, \\ \exp(y_{m,m}) & \mbox{if } m = n, \mbox{ and} \\ y_{m,n} & \mbox{if } m > n. \end{array} \right. \]

Absolute Jacobian Determinant of Cholesky Factor Inverse Transform

The transform has a diagonal Jacobian matrix, the absolute determinant of which is

\[ \prod_{n=1}^N \frac{\partial}{\partial_{y_{n,n}}} \, \exp(y_{n,n}) \ = \ \prod_{n=1}^N \exp(y_{n,n}) \ = \ \prod_{n=1}^N x_{n,n}. \]

Let \(x = f^{-1}(y)\) be the inverse transform from a \(N + \binom{N}{2} + (M - N)N\) vector to an \(M \times N\) Cholesky factor for a covariance matrix \(x\) defined in the previous section. A density function \(p_X(x)\) defined on \(M \times N\) Cholesky factors of covariance matrices is transformed to the density \(p_Y(y)\) over \(N + \binom{N}{2} + (M - N)N\) vectors \(y\) by

\[ p_Y(y) = p_X(f^{-1}(y)) \prod_{N=1}^N x_{n,n}. \]