Projected Normal Distribution: Moment Approximations and Generalizations

TL;DR

This paper derives analytic moment approximations for the projected normal distribution and its generalizations, enabling better data modeling and analysis in neuroscience and related fields.

Contribution

It introduces new moment approximation formulas for the projected normal distribution and its generalizations involving quadratic forms, expanding their practical applicability.

Findings

Moment approximations are accurate across various dimensions.

The methods enable effective data fitting via moment matching.

Generalizations extend the distribution's flexibility for modeling.

Abstract

The projected normal distribution, also known as the angular Gaussian distribution, is obtained by dividing a multivariate normal random variable $\mathbf{x}$ by its norm $\sqrt{\mathbf{x}^T \mathbf{x}}$. The resulting random variable follows a distribution on the unit sphere. No closed-form formulas for the moments of the projected normal distribution are known, which can limit its use in some applications. In this work, we derive analytic approximations to the first and second moments of the projected normal distribution using Taylor expansions and using results from the theory of quadratic forms of Gaussian random variables. Then, motivated by applications in systems neuroscience, we present generalizations of the projected normal distribution that divide the variable $\mathbf{x}$ by a denominator of the form $\sqrt{\mathbf{x}^T \mathbf{B} \mathbf{x} + c}$, where $\mathbf{B}$ is a symmetric positive definite matrix and $c$ is a non-negative number. We derive moment approximations as well as the density function for these other projected distributions. We show that the moments approximations are accurate for a wide range of dimensionalities and distribution parameters. Furthermore, we show that the moments approximations can be used to fit these distributions to data through moment matching. These moment matching methods should be useful for analyzing data across a range of applications where the projected normal distribution is used, and for applying the projected normal distribution and its generalizations to model data in neuroscience.