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Related papers: Estimating prevalence with precision and accuracy

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An accurate multiclass classification strategy is crucial to interpreting antibody tests. However, traditional methods based on confidence intervals or receiver operating characteristics lack clear extensions to settings with more than two…

Quantitative Methods · Quantitative Biology 2024-05-07 Rayanne A. Luke , Anthony J. Kearsley , Paul N. Patrone

We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors. While existing solutions in this context are based on…

Methodology · Statistics 2026-03-11 David T. Frazier , Christopher Drovandi , Robert Kohn

Estimation of extreme quantile regions, spaces in which future extreme events can occur with a given low probability, even beyond the range of the observed data, is an important task in the analysis of extremes. Existing methods to estimate…

Methodology · Statistics 2020-10-28 Boris Beranger , Simone A. Padoan , Scott A. Sisson

With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common goal in uncertainty quantification (UQ) in machine learning…

Machine Learning · Computer Science 2021-09-22 Youngseog Chung , Ian Char , Han Guo , Jeff Schneider , Willie Neiswanger

For the binary prevalence quantification problem under prior probability shift, we determine the asymptotic variance of the maximum likelihood estimator. We find that it is a function of the Brier score for the regression of the class label…

Machine Learning · Statistics 2021-09-23 Dirk Tasche

Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course…

Machine Learning · Computer Science 2021-08-20 Matias Valdenegro-Toro

Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct…

Machine Learning · Computer Science 2023-11-21 Ruxiao Duan , Brian Caffo , Harrison X. Bai , Haris I. Sair , Craig Jones

Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and…

Atmospheric and Oceanic Physics · Physics 2022-04-06 Michael F. Howland , Oliver R. A. Dunbar , Tapio Schneider

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and…

Machine Learning · Computer Science 2025-06-04 Shubhendu Trivedi , Brian D. Nord

In this paper, we propose standard statistical tools as a solution to commonly highlighted problems in the explainability literature. Indeed, leveraging statistical estimators allows for a proper definition of explanations, enabling…

Machine Learning · Statistics 2024-05-01 Valentina Ghidini

A critical literature review and comprehensive simulation study is used to show that (a) non-parametric bootstrap is a viable alternative to commonly taught and used methods in basic estimation tasks (mean, variance, quartiles, correlation)…

Methodology · Statistics 2025-10-16 Urša Zrimšek , Erik Štrumbelj

Until recently, uncertainty quantification in low energy nuclear theory was typically performed using frequentist approaches. However in the last few years, the field has shifted toward Bayesian statistics for evaluating confidence…

Nuclear Theory · Physics 2019-07-24 G. B. King , A. E. Lovell , L. Neufcourt , F. M. Nunes

While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of these models is the uncertainty quantification surrounding…

Machine Learning · Computer Science 2025-11-17 Giorgio Morales , Frederic Jurie , Jalal Fadili

Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones. In this work we study the calibration of uncertainty prediction for…

Machine Learning · Computer Science 2020-02-04 Dan Levi , Liran Gispan , Niv Giladi , Ethan Fetaya

Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP)…

Machine Learning · Computer Science 2024-01-05 Hamed Karimi , Reza Samavi

Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the development of…

Image and Video Processing · Electrical Eng. & Systems 2025-10-07 Canberk Ekmekci , Mujdat Cetin

We present a Bayesian perspective on quantifying the uncertainty of graph signals estimated or reconstructed from imperfect observations. We show that many conventional methods of graph signal estimation, reconstruction and imputation, can…

Signal Processing · Electrical Eng. & Systems 2025-05-22 Lennard Rompelberg , Michael T. Schaub

From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models. However, the impressive generalization performance of neural networks…

Machine Learning · Computer Science 2021-02-09 Taejong Joo , Uijung Chung

Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic classifier. We call this approach robustness…

Machine Learning · Computer Science 2025-04-11 Adrián Detavernier , Jasper De Bock

Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of efficiently finding a global optimum of an expensive-to-evaluate black-box function. In general, a…

Machine Learning · Computer Science 2025-07-29 Jungtaek Kim