Efficient methods for Gaussian Markov random fields under sparse linear constraints
Methods for inference and simulation of linearly constrained Gaussian MarkovRandom Fields (GMRF) are computationally prohibitive when the number ofconstraints is large. In some cases, such as for intrinsic GMRFs, they may even beunfeasible. We propose a new class of methods to overcome these challenges in the common case of sparse constraints, where one has a large number of constraints and each o
