Differentiability and Approximation of Probability Functions under Gaussian Mixture Models: A Bayesian Approach

TL;DR

This paper develops a Bayesian approach to analyze and approximate probability functions in Gaussian mixture models by extending spherical radial decomposition, establishing differentiability conditions, and demonstrating improved numerical approximations.

Contribution

It introduces a novel Bayesian framework that extends spherical radial decomposition to Gaussian mixture models, enabling differentiability analysis and efficient approximation of probability functions.

Findings

Established conditions for differentiability of probability functions.

Provided integral representation of the gradient of the probability function.

Demonstrated numerical advantages over classical sampling methods.

Abstract

In this work, we study probability functions associated with Gaussian mixture models. Our primary focus is on extending the use of spherical radial decomposition for multivariate Gaussian random vectors to the context of Gaussian mixture models, which are not inherently spherical but only conditionally so. Specifically, the conditional probability distribution, given a random parameter of the random vector, follows a Gaussian distribution, allowing us to apply Bayesian analysis tools to the probability function. This assumption, together with spherical radial decomposition for Gaussian random vectors, enables us to represent the probability function as an integral over the Euclidean sphere. Using this representation, we establish sufficient conditions to ensure the differentiability of the probability function and provide and integral representation of its gradient. Furthermore, leveraging the Bayesian decomposition, we approximate the probability function using random sampling over the parameter space and the Euclidean sphere. Finally, we present numerical examples that illustrate the advantages of this approach over classical approximations based on random vector sampling.