24 papers · ranked by Valyu relevance
Swarup Poria, Aman Dhiman
The method of variation of parameter (VOP) for solving linear ordinary differential equation is revisited in this article. Historically, Lagrange and Euler explained the method of variation of parameter in the context of perturbation method. In this article, we explain the construction of particular solutions of a…
Bahare Salmani, Joost-Pieter Katoen
This paper addresses the -close parameter tuning problem for Bayesian networks (BNs): find a minimal -close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid. Based on the state-of-the-art "region verification"…
Petr Lehner, Petr Konečný, Pratanu Ghosh, Jacek Gołaszewski
The numerical modelling of chloride penetration into concrete is very sensitive to the correct description of the input data. In the recent era, high-performance concrete (HPC), which combines Portland cement and other supplementary cementitious materials, has been gaining attraction due to their desirable material…
Shu‐Hui Hsu, Ravi Kulasekere, Peter L. Roberson
Film calibration is time-consuming work when dose accuracy is essential while working in a range of photon scatter environments. This study uses the single-target single-hit model of film response to fit the calibration curves as a function of calibration method, processor condition, field size and depth. Kodak XV film…
Nicolas Christou, Ivo D. Dinov, Alejandro Raul Hernandez Montoya
Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates.…
Stanley Luck, Alan D Hutson
We consider the problem of constructing a complete set of parameters that account for all of the degrees of freedom for point-biserial variation. We devise an algorithm where sort as an intrinsic property of both numbers and labels, is used to generate the parameters. Algebraically, point-biserial variation is…
Joel P. Heath, Peter Borowski, Shu-Dong Zhang
Real quantities can undergo such a wide variety of dynamics that the mean is often a meaningless reference point for measuring variability. Despite their widespread application, techniques like the Coefficient of Variation are not truly proportional and exhibit pathological properties. The non-parametric measure…
M. Pahlevani
The variance, the average of squared deviations of data values from their mean, is the most widely used criterion for measuring the variation. Small amounts of the variance indicate that the values tend to be close to the mean and its high amounts are indicative of more dispersion around the mean. However, when dealing…
Authors not listed
This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
Andrew Gelman, Jessica Hullman, Lauren Kennedy
The average causal effect can often be best understood in the context of its variation. We demonstrate with two sets of four graphs, all of which represent the same average effect but with much different patterns of heterogeneity. As with the famous correlation quartet of Anscombe (1973), these graphs dramatize the way…
Marco Cardis, Maura Casadio, Rajiv Ranganathan
Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning is not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it…
André Chalom, Paulo Inácio Prado
| 1 | | | Introduction | | 3 | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1.1 Parameter spaces 1.2 Applications of parameter space exploration . . . 2 Sampling Techniques 2.1 Latin Hypercube: Definition and use 2.2 Algorithms and extensions 2.2.1 Note…
Zoltán Botta-Dukát
Comparing within-species variations of traits can be used in testing ecological theories. In these comparisons, it is useful to remove the effect of the difference in mean trait values, therefore measures of relative variation, most often the coefficient of variation (CV), are used. The studied traits are often…
Md Mahmudul Hasan Khan, Mohd Y. Rafii, Shairul Izan Ramlee, Mashitah Jusoh + 1 more
'Mashitah Jusoh' 'Md Al Mamun'] This investigation was carried out to explore G × E interaction for yield and its associated attributes in 30 Bambara groundnut genotypes across four environments in tropical Malaysia. Such evaluations are essential when the breeding program's objective is to choose genotypes with broad…
Authors not listed
Physics-based methods such as protein-ligand binding free energy calculations have been increasingly adopted in early-stage drug discovery to prioritize promising compounds for synthesis. However, the accuracy of these methods is highly dependent on details of the calculation and choices made while preparing the…
Holger Schielzeth, Shinichi Nakagawa
Individuals differ in average phenotypes and in sensitivity to environmental variation. Such context-sensitivity can be modelled as random-slope variation. Random-slope variation implies that the proportion of between-individual variation varies across the range of a covariate (environment/context/time/age) and has…
Zac P. Robinson, James Steele, Eric R. Helms, Eric T. Trexler + 8 more
There is growing emphasis on investigating heterogeneity in resistance training (RT) outcomes, likely motivated by observations of substantial gross variability in training effects. However, gross variability does not necessarily represent true inter-individual response variation (IRV) and can be obscured by…
Fintan Costello, Paul Watts
Estimates of uncertainty or variance in experimental means are central to physics. This is especially the case for 'world averages' of fundamental parameters in particle physics, which aggregate results from a number of experiments to express current knowledge about these parameters and where variances in these world…
Julien F. Ayroles, Sean M. Buchanan, Chelsea Jenney, Kyobi Skutt-Kakaria + 4 more
Variability is ubiquitous in nature and a fundamental feature of complex systems. Few studies, however, have investigated variance itself as a trait under genetic control. By focusing primarily on trait means and ignoring the effect of alternative alleles on trait variability, we may be missing an important axis of…
Zhenghao Wu, Tianhang Zhou
In the realm of multiscale molecular simulations, structure-based coarse graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems such as polymers. This involves optimizing CG interactions by matching static correlation functions of corresponding degrees of…
Burcu Ozdemir, Miguel Hernández-del-Valle, Maggie Gaunt, Christina Schenk + 4 more
The development of new thermoplastic-based nanocomposites for, as well as using, 3D printing requires extensive experimental testing. One typically goes through many failed, or otherwise sub-optimal, iterations before finding acceptable solutions (e.g. compositions, 3D printing parameters). It is desirable to reduce…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Alexander Volkmann
Recently, the variability ratio (VR) effect size statistic has been used with increasing frequency in the study of differences in variation of a measured variable between two study populations. More specifically, the VR effect size statistic allows for the detection of treatment effect heterogeneity (TEH) of medical…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…