![[2602.10706] Reducing Estimation Uncertainty Using Normalizing Flows and Stratification](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.10706] Reducing Estimation Uncertainty Using Normalizing Flows and Stratification
Summary
This paper presents a novel approach to reducing estimation uncertainty in statistical analysis using normalizing flows and stratified sampling, outperforming traditional methods.
Why It Matters
The research addresses a critical challenge in statistical estimation, particularly when traditional (semi-)parametric assumptions fail. By introducing a flexible model that adapts to unknown data distributions, this work has significant implications for various applications in machine learning and data science, enhancing the reliability of estimations.
Key Takeaways
- Proposes a flow-based model integrated with stratified sampling.
- Demonstrates significant reduction in estimation uncertainty.
- Outperforms traditional Monte Carlo estimators and Gaussian mixture models.
- Applicable to high-dimensional datasets, enhancing flexibility.
- Reproducible code is provided for further exploration.
Computer Science > Machine Learning arXiv:2602.10706 (cs) [Submitted on 11 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v3)] Title:Reducing Estimation Uncertainty Using Normalizing Flows and Stratification Authors:Paweł Lorek, Rafał Nowak, Rafał Topolnicki, Tomasz Trzciński, Maciej Zięba, Aleksandra Krystecka View a PDF of the paper titled Reducing Estimation Uncertainty Using Normalizing Flows and Stratification, by Pawe{\l} Lorek and 5 other authors View PDF Abstract:Estimating the expectation of a real-valued function of a random variable from sample data is a critical aspect of statistical analysis, with far-reaching implications in various applications. Current methodologies typically assume (semi-)parametric distributions such as Gaussian or mixed Gaussian, leading to significant estimation uncertainty if these assumptions do not hold. We propose a flow-based model, integrated with stratified sampling, that leverages a parametrized neural network to offer greater flexibility in modeling unknown data distributions, thereby mitigating this limitation. Our model shows a marked reduction in estimation uncertainty across multiple datasets, including high-dimensional (30 and 128) ones, outperforming crude Monte Carlo estimators and Gaussian mixture models. Reproducible code is available at this https URL. Comments: Subjects: Machine Learning (cs.LG) MSC classes: 65C05 Cite as: arXiv:2602.10706 [cs.LG] (or arXiv:2602.10706v3 [cs.LG] for this version) https://d...
