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This work aims to assess the accuracy of diffusion models in estimating probability densities for specific distributions.

April 19, 2026Open Access

Quantifying density estimation error in diffusion models

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Experimental evaluation of diffusion models for density estimation in rare event tasks, indicating limitations.

This work aims to assess the accuracy of diffusion models in estimating probability densities for specific distributions.
Evaluated two distributions: Gaussian and Brownian motion, both in two dimensions.
Applied a conditional analysis with samples outside a forbidden region near the origin.
Examined the effects of various parameters on estimation accuracy using trained diffusion models.
Used ODE integrator and both histogram- and ICOV-based density estimation methods.
Training effort was identified as the dominant factor affecting estimation accuracy.
Diffusion models failed to achieve satisfactory accuracy for rare event simulation tasks in low-dimensional scenarios.

Justice Sefas (2026) studied this question.

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