Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics

• URL: http://arxiv.org/abs/2602.24201v1
• Date: Fri, 27 Feb 2026 17:27:55 GMT
• Title: Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
• Authors: Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan Günnemann, Andrea Dittadi, Fabian J. Theis,
• Abstract summary: We leverage condition-aware flow matching to derive a single dynamical formulation for tracking density ratios along generative trajectories.<n>We demonstrate competitive performance on simulated benchmarks for closed-form ratio estimation, and show that our method supports versatile tasks in single-cell genomics data analysis.
• Score: 80.05951561886123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled comparisons of sample likelihoods under different data-generating processes across conditions and covariates. While exact-likelihood models such as normalizing flows offer a promising approach to density ratio estimation, naive flow-based evaluations are computationally expensive, as they require simulating costly likelihood integrals for each distribution separately. In this work, we leverage condition-aware flow matching to derive a single dynamical formulation for tracking density ratios along generative trajectories. We demonstrate competitive performance on simulated benchmarks for closed-form ratio estimation, and show that our method supports versatile tasks in single-cell genomics data analysis, where likelihood-based comparisons of cellular states across experimental conditions enable treatment effect estimation and batch correction evaluation.

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