Suppose that Q is a sparse covariance matrix, and I want to evaluate \mathbf{y} \sim N(\boldsymbol{0},Q). Can I leverage the sparsity of Q? Right now, I have Q as a `SparseMatrixCSC{Float64, Int64}`

. Roughly 97% of the matrix is 0.

When I use `pdf(MvNormal(zeros(n), Q), y)`

, I get the following error: `MethodError: no method matching PDMats.PDMat(::SparseMatrixCSC{Float64, Int64}, ::SuiteSparse.CHOLMOD.Factor{Float64})`

. It seems that `MvNormal()`

does not accept a sparse matrix as the covariance matrix because of some incompatibility in the `PDMats`

package. For the time begin, I am using `pdf(MvNormal(zeros(n), Hermitian(Array(Cov_y_star))), y)`

. However, this seems incredibly inefficient.

Do you know of a way that I can leverage the sparsity of Q to improve the computational speed in evaluating a multivariate normal distribution?