  Algorithms for Gaussian process, marginal likelihood methods or restricted
maximum likelihood methods often require derivatives of log determinant terms.
These log determinants are usually parametric with variance parameters of the
underlying statistical models. This paper demonstrates that, when the
underlying matrix is sparse, how to take the advantage of sparse
inversion---selected inversion which share the same sparsity as the original
matrix---to accelerate evaluating the derivative of log determinant.
