Mixture Modeling with Normalizing Flows for Spherical Density Estimation

TL;DR

This paper introduces a mixture-of-normalizing-flows model for flexible density estimation on the sphere, addressing limitations of existing models and demonstrating effectiveness on simulated and real-world Earth surface data.

Contribution

It presents a novel mixture model using normalizing flows on the sphere, enhancing flexibility over existing parametric and nonparametric methods.

Findings

Model outperforms single-component flows on simulated data.

Accurately models earthquake event densities.

Effectively captures terrorist activity distribution.

Abstract

Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years. Many flexible families of normalizing flows have been developed. However, the focus to date has largely been on normalizing flows on Euclidean domains; while normalizing flows have been developed for spherical and other non-Euclidean domains, these are generally less flexible than their Euclidean counterparts. To address this shortcoming, in this work we introduce a mixture-of-normalizing-flows model to construct complicated probability density functions on the sphere. This model provides a flexible alternative to existing parametric, semiparametric, and nonparametric, finite mixture models. Model estimation is performed using the expectation maximization algorithm and a variant thereof. The model is applied to simulated data, where the benefit over the conventional (single component) normalizing flow is verified. The model is then applied to two real-world data sets of events occurring on the surface of Earth; the first relating to earthquakes, and the second to terrorist activity. In both cases, we see that the mixture-of-normalizing-flows model yields a good representation of the density of event occurrence.