Stochastic sampling of the hyperspherical von mises?fisher distribution without rejection methods

We propose a novel sampling algorithm for the von Mises-Fisher distribution on the unit hypersphere. Unlike previous works, we show a solution for an arbitrary number of dimensions without requiring rejection sampling. As a result, the proposed algorithm has a deterministic runtime. The key idea consists in applying the inversion method to a one-dimensional subproblem and analytically calculating the integral occurring in the distribution function. The proposed method is most efficient for odd numbers of dimensions. We compare the algorithm to a state-of-the-art rejection sampling method in simulations.