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We propose a parallelizable sparse inverse formulation Gaussian process(SpInGP) for temporal models. It uses a sparse precision GP formulation andsparse matrix routines to speed up the computations. Due to the state-spaceformulation used in the algorithm, the time complexity of the basic SpInGP islinear, and because all the computations are parallelizable, the parallel formof the algorithm is sublinear in the number of data points. We provide examplealgorithms to implement the sparse matrix routines and experimentally test themethod using both simulated and real data.

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)