discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance

• URL: http://arxiv.org/abs/2511.15854v1
• Date: Wed, 19 Nov 2025 20:23:11 GMT
• Title: discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
• Authors: Steven Adams, Elize Alwash, Luca Laurenti,
• Abstract summary: discretize_distributions is a Python package that constructs discrete approximations of Gaussian mixture distributions.<n>We show that discretize_distributions produces accurate approximations at low computational cost.
• Score: 5.020978032396304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present discretize_distributions, a Python package that efficiently constructs discrete approximations of Gaussian mixture distributions and provides guarantees on the approximation error in Wasserstein distance. The package implements state-of-the-art quantization methods for Gaussian mixture models and extends them to improve scalability. It further integrates complementary quantization strategies such as sigma-point methods and provides a modular interface that supports custom schemes and integration into control and verification pipelines for cyber-physical systems. We benchmark the package on various examples, including high-dimensional, large, and degenerate Gaussian mixtures, and demonstrate that discretize_distributions produces accurate approximations at low computational cost.

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