25 papers · ranked by Valyu relevance
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…
Hao Tang, Tianle Yue, Ying Li
Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel…
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…
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…
Józef Wiora, Alicja Wiora
An assessment of measurement uncertainty is a task, which has to be the final step of every chemical assay. Apart from a commonly applied typical assessment method, Monte Carlo (MC) simulations may be used. The simulations are frequently performed by a computer program, which has to be written, and therefore some…
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…
Umang Bhatt, Yunfeng Zhang, Javier Antorán, Q. Vera Liao + 10 more
'Prasanna Sattigeri' 'Riccardo Fogliato' 'Gabrielle Gauthier Melançon' 'Ranganath Krishnan' 'Jason Stanley' 'Omesh Tickoo' 'Lama Nachman' 'Rumi Chunara' 'Adrian Weller' 'Alice Xiang'] Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving…
A. R. Colclough
Foundational questions in statistics are notoriously controversia1.[fn2-jresv92n3p167_a1b] Nowhere is this more true than in error theory which presents special problems not usually encountered in other fields of statistical practice. In particular, it often invites the experimenter, when estimating experimental…
Fahimeh Fakour, Ali Mosleh, Ramin Ramezani
Deep Learning Authors: ['Fahimeh Fakour' 'Ali Mosleh' 'Ramin Ramezani'] The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and…
You Wu, F. Owen Hoffman, A. Iulian Apostoaei, Deukwoo Kwon + 3 more
'Brian A. Thomas' 'Racquel Glass' 'Lydia B. Zablotska'] Background Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the…
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…
Gongcheng Liu, Huimin Wang, Yanlin Han, Chunlong Liu + 1 more
The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used…
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…
Nan Chen, Stephen Wiggins, Marios Andreou
Quantification -- A Tutorial for Beginners Authors: ['Nan Chen' 'Stephen Wiggins' 'Marios Andreou'] George Box, a British statistician, wrote the famous aphorism, "All models are wrong, but some are useful." The aphorism acknowledges that models, regardless of qualitative, quantitative, dynamical, or statistical…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Branimir Zauner, Branko Petrinec, Tomislav Bituh, Saša Ceci + 4 more
'Nikola Volarić' 'Aleksandar Včev' 'Andrea Vukoja' 'Dinko Babić'] Title: Abstract We present an overview of the theory of random measurement errors, focusing on the underlying concepts rather than on a strict mathematical formulation. Although the related literature is extensive, one can frequently encounter partly or…
Authors not listed
Bonkowski and De Souza [Sol. Stat. Ionics 429, 116967 (2025)] provide a guide for performing molecular dynamics simulations of ion transport, including methods for estimating diffusion coefficients and their uncertainties from mean-squared displacement (MSD) data. The discussion of uncertainty in estimated diffusion…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
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…
Simon Cramer, Tobias Müller, Robert Schmitt
virtual measurements Authors: ['Simon Cramer' 'Tobias Müller' 'Robert Schmitt'] In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections…
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…
Zhe Liu
Traditionally regression analysis answers questions about the relationships among variables based on the assumption that the observation values of variables are precise numbers. It has long been dominated by least squares techniques, mostly due to their elegant theoretical foundation and ease of implementation.…
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’…
Daniel Wallach, Taru Palosuo, Henrike Mielenz, Samuel Buis + 31 more
Crop phenology has a major influence on crop yield and is a major aspect of crop response to global warming. Process-based models of phenology are often used to predict the effect of weather on the development rate of crops through their growth phases, but such models are associated with large uncertainties, as…
Rowan Iskandar
Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision-relevant quantities. However, current uncertainty quantification…