First and Second Moments and Fractional Anisotropy of General von Mises-Fisher and Peanut Distributions

Abstract: Spherical distributions, in particular, the von Mises-Fisher distribution, are often used for problems using or modelling directional data. Since expectation and variance-covariance matrices follow from the first and second moments of the spherical distribution, the moments often need to be approximated numerically by computing trigonometric integrals. Here, we derive the explicit forms of the first and second moments for an n-dimensional von Mises-Fisher and peanut distributions by making use of the divergence theorem in the calculations. The derived formulas can be easily used in simulations, significantly decreasing the computation time. Moreover, we compute the fractional anisotropy formulas for the diffusion tensors derived from the bimodal von Mises-Fisher and peanut distributions, and show that the peanut distribution is limited in the amount of anisotropy it permits, making the von Mises-Fisher distribution a better choice when modelling anisotropy.