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22 papers · ranked by Valyu relevance

01
PPubMed24 Jan 2018
The principles and methods behind EFSA 's Guidance on Uncertainty Analysis in Scientific Assessment
Diane Benford, Thorhallur Halldorsson, Michael John Jeger, Helle Katrine Knutsen + 23 more
'Helle Katrine Knutsen' 'Simon More' 'Hanspeter Naegeli' 'Hubert Noteborn' 'Colin Ockleford' 'Antonia Ricci' 'Guido Rychen' 'Josef R Schlatter' 'Vittorio Silano' 'Roland Solecki' 'Dominique Turck' 'Maged Younes' 'Peter Craig' 'Andrew Hart' 'Natalie Von Goetz' 'Kostas Koutsoumanis' 'Alicja Mortensen' 'Bernadette…
92relevance
02
CchemRxiv15 Jan 2026
Uncertainty Quantification for In Silico Chemistry
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
91relevance
03
BbioRxiv5 Mar 2018
Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience
Simen Tennøe, Geir Halnes, Gaute T. Einevoll
Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods…
90relevance
04
AarXiv5 Jan 2025
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Tianyang Wang, Yunze Wang, Jun Zhou, Benji Peng + 15 more
Techniques in Artificial Intelligence Authors: ['Tianyang Wang' 'Yunze Wang' 'Jun Zhou' 'Benji Peng' 'Xinyuan Song' 'Charles Zhang' 'Xintian Sun' 'Qian Niu' 'Junyu Liu' 'Silin Chen' 'Keyu Chen' 'Ming Li' 'Peiyong Feng' 'Ziqian Bi' 'Ming Liu' 'Yichao Zhang' 'Fei Cheng' 'Caitlyn Heqi Yin' 'Lingzhi Yan'] > aUniversity of…
88relevance
05
PPubMed26 Jul 2024
An Application of Uncertainty Quantification to Efficiency Measurements and Validating Requirements through Correlating Simulation and Physical Testing Results †
Michael Leighton, Uday Akasapu, Omprakash Kaiwartya
Validation is a critical aspect of product development for meeting design goals and mitigating risk in the face of considerable cost and time commitments. In this research article, uncertainty quantification (UQ) for efficiency testing of an Electric Drive Unit (EDU) is demonstrated, considering confidence in…
88relevance
06
BbioRxiv4 Apr 2018
Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models
Olivia Eriksson, Alexandra Jauhiainen, Sara Maad Sasane, Andrei Kramer + 3 more
Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as…
88relevance
07
PPubMed13 Jul 2024
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson + 2 more
'Jasper Slingsby' 'Tamara B. Robinson'] Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the…
87relevance
08
BbioRxiv15 Feb 2023
Spectral methods for prediction uncertainty quantification in Systems Biology
Anna Deneer, Jaap Molenaar, Christian Fleck
Uncertainty is ubiquitous in biological systems. These uncertainties can be the result of lack of knowledge or due to a lack of appropriate data. Additionally, the natural variability of biological systems caused by intrinsic noise, e.g. in stochastic gene expression, leads to uncertainties. With the help of numerical…
87relevance
09
AarXiv2022
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim + 39 more
'Janne Cathrin Hetle Aspheim' 'Claudia Battistin' 'Nicola Bulso' 'Hannah Christensen' 'Benjamin Cretois' 'Ryan John Cubero' 'Ivan A. Davidovich' 'Lisa Dickel' 'Benjamin Dunn' 'Etienne Dunn‐Sigouin' 'Karin Dyrstad' 'Sigurd Einum' 'Donata Giglio' 'Haakon Gjerløw' 'Amélie Godefroidt' 'Ricardo Gonzalez-Gil' 'Soledad…
84relevance
10
AarXiv2023
Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry
Babajide Kolade
Simulations using machine learning (ML) models and mechanistic models are often run to inform decision-making processes. Uncertainty estimates of simulation results are critical to the decisionmaking process because simulation results of specific scenarios may have wide, but unspecified, confidence bounds that may…
84relevance
11
PPubMed7 Mar 2022
Bayesian calibration, process modeling and uncertainty quantification in biotechnology Bayesian calibration, process modeling and uncertainty quantification in biotechnology
Laura Marie Helleckes, Michael Osthege, Wolfgang Wiechert, Eric von Lieres + 2 more
'Eric von Lieres' 'Marco Oldiges' 'Dina Schneidman-Duhovny'] High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and…
84relevance
12
CchemRxiv8 Apr 2025
aweSOM: a GNN-based Site-of-Metabolism Predictor with Aleatoric and Epistemic Uncertainty Estimation
Authors not listed
Accurate determination of the metabolic fate of xenobiotics is essential for ensuring their safety and efficacy. While in vivo and in vitro methods remain the gold standard for assessing metabolic properties, they are both costly and time-consuming. In silico metabolism prediction models offer complementary solutions…
83relevance
13
PPubMed5 Nov 2022
Insights into the quantification and reporting of model-related uncertainty across different disciplines
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim + 39 more
'Janne Cathrin Hetle Aspheim' 'Claudia Battistin' 'Nicola Bulso' 'Hannah M. Christensen' 'Benjamin Cretois' 'Ryan Cubero' 'Iván A. Davidovich' 'Lisa Dickel' 'Benjamin Dunn' 'Etienne Dunn-Sigouin' 'Karin Dyrstad' 'Sigurd Einum' 'Donata Giglio' 'Haakon Gjerløw' 'Amélie Godefroidt' 'Ricardo González-Gil' 'Soledad Gonzalo…
82relevance
14
CchemRxiv8 Sep 2023
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
82relevance
15
CchemRxiv21 Oct 2025
Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
80relevance
16
AarXiv2019
Measurement Errors in R
Iñaki Ucar, Edzer Pebesma, Arturo Azcorra
This paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the errors package, which provides a class for associating…
76relevance
17
CchemRxiv30 Nov 2023
Advancing Odor Classification Models Enhanced by Scientific Machine Learning and Mechanistic Model: Probabilistic Weight Assignment for Odor Intensity Prediction and Uncertainty Analysis for Robust Fragrance Classification
Vinicius Santana, Erbet Costa, Carine Rebello, Ana Mafalda Ribeiro + 2 more
This study presents an innovative framework for classifying and predicting odor intensity in perfumery, combining scientific machine learning with mechanistic modeling to enhance fragrance design precision. A probabilistic weight assignment is introduced, utilizing scent classifier outputs to determine the contribution…
76relevance
18
BbioRxiv12 Apr 2022
Bayesian Parameter Estimation for Dynamical Models in Systems Biology
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and ‘-omics’…
76relevance
19
AarXiv2022
The theory of quantitative trading
Andrea Berdondini
| Introduction | 4 | | --- | --- | | Structure of the book | 5 | | Statistics | 6 | | The uncertainty of the statistical data | 7 | | The information paradox | 12 | | Use of the fundamental problem of statistics to define the validity limit ofaaaaaaalaa | | | Occam's razor principle | 16 | | Resolution of the St.…
75relevance
20
BbioRxiv28 Aug 2024
Modeling Decision-Making Under Uncertainty with Qualitative Outcomes
Nachshon Korem, Duek Or, Jia Rounan, Emily Wertheimer + 3 more
Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to model uncertainty attitudes in decisions with qualitative outcomes. Participants made choices between certain outcomes and…
72relevance
21
AarXiv19 Aug 2022
Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments
Karel Kok, Burkhard Priemer
Interpreting experimental data in high school experiments can be a difficult task for students, especially when there is large variation in the data. At the same time, calculating the standard deviation poses a challenge for students. In this article, we look at alternative uncertainty measures to describe the…
69relevance
22
CchemRxiv3 May 2023
Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas production sites that uses concentration data collected by pointin-space continuous monitoring systems (CMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and…
67relevance

Results for uncertainty quantification

The principles and methods behind EFSA 's Guidance on Uncertainty Analysis in Scientific Assessment

Uncertainty Quantification for In Silico Chemistry

Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

An Application of Uncertainty Quantification to Efficiency Measurements and Validating Requirements through Correlating Simulation and Physical Testing Results †

Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Spectral methods for prediction uncertainty quantification in Systems Biology

How is model-related uncertainty quantified and reported in different disciplines?

Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry

Bayesian calibration, process modeling and uncertainty quantification in biotechnology Bayesian calibration, process modeling and uncertainty quantification in biotechnology

aweSOM: a GNN-based Site-of-Metabolism Predictor with Aleatoric and Epistemic Uncertainty Estimation

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Measurement Errors in R

Advancing Odor Classification Models Enhanced by Scientific Machine Learning and Mechanistic Model: Probabilistic Weight Assignment for Odor Intensity Prediction and Uncertainty Analysis for Robust Fragrance Classification

Bayesian Parameter Estimation for Dynamical Models in Systems Biology

The theory of quantitative trading

Modeling Decision-Making Under Uncertainty with Qualitative Outcomes

Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments

Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems

The principles and methods behind EFSA 's Guidance on Uncertainty Analysis in Scientific Assessment

Uncertainty Quantification for In Silico Chemistry

Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

An Application of Uncertainty Quantification to Efficiency Measurements and Validating Requirements through Correlating Simulation and Physical Testing Results †

Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Spectral methods for prediction uncertainty quantification in Systems Biology

How is model-related uncertainty quantified and reported in different disciplines?

Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry

Bayesian calibration, process modeling and uncertainty quantification in biotechnology Bayesian calibration, process modeling and uncertainty quantification in biotechnology

aweSOM: a GNN-based Site-of-Metabolism Predictor with Aleatoric and Epistemic Uncertainty Estimation

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Measurement Errors in R

Advancing Odor Classification Models Enhanced by Scientific Machine Learning and Mechanistic Model: Probabilistic Weight Assignment for Odor Intensity Prediction and Uncertainty Analysis for Robust Fragrance Classification

Bayesian Parameter Estimation for Dynamical Models in Systems Biology

The theory of quantitative trading

Modeling Decision-Making Under Uncertainty with Qualitative Outcomes

Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments

Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems