This is part three of an ongoing exercise in hubris. Part one is here.Part two is here. The overall aim of this series of posts is to look at how sparse Cholesky factorisations work, how JAX works, and how to marry the two with the ultimate aim of putting a bit of sparse matrix support into PyMC, which should allow for faster inference in linear mixed models, Gaussian spatial models. And hopefully, if anyone ever gets around to putting the Laplace approximation in, all sorts of GLMMs and non-...
