• bootlib

  • Referenced in 408 articles [sw40642]
  • Plus. A library of functions and data written by Angelo Canty to accompany the Cambridge ... errors, confidence intervals, and other measures of uncertainty for a wide range of problems. This ... nonlinear models, and time series, spatial data and point processes. Chapter 9 describes how variance...
  • SmartMobile

  • Referenced in 15 articles [sw10867]
  • does not take into account uncertainty in the input data. The program SmartMobile enhances ... correct and measure the influence of data uncertainty. In this paper, we outline the main...
  • PSQL

  • Referenced in 8 articles [sw02180]
  • However, in all real-world environments, uncertainty in data values is a common occurrence. Therefore ... extend the relational model so that data uncertainty can be captured explicitly. In an earlier...
  • EVPI

  • Referenced in 20 articles [sw02644]
  • decisions have to be made under future uncertainty. There are a variety of methods ... realisations of the random data process determining the uncertainty in the problem. This paper describes ... linear programmes which either have too many data paths to solve directly or a continuous...
  • DUE

  • Referenced in 3 articles [sw27796]
  • Data Uncertainty Engine (DUE): A software tool for assessing and simulating uncertain environmental variables. This ... software tool for: (1) assessing uncertainties in environmental data; and (2) generating realisations of uncertain ... uncertainty propagation analyses: the “Data Uncertainty Engine (DUE)”. Data may be imported into DUE from ... variables (e.g. land-cover). Once data are imported, an uncertainty model can be developed...
  • Virtual library

  • Referenced in 23 articles [sw27598]
  • Emulation/ Prediction, Uncertainty Quantification, Multi Fidelity Simulation, Calibration/ Tuning, Screening, Integration, Functional Data...
  • ProbCD

  • Referenced in 5 articles [sw20338]
  • ProbCD: enrichment analysis accounting for categorization uncertainty. As in many other areas of science, systems ... contingency tables, a type of categorical data analysis known in this field as enrichment (also ... Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment ... address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis...
  • GRESS

  • Referenced in 13 articles [sw00385]
  • sensitivity analysis of Fortran programs. Sensitivity and uncertainty analyses are necessary components in the evaluation ... data needs and as a first step in a thorough assessment of parameter uncertainties. This ... selected results with respect to all input data. GRESS computes a normalized sensitivity by multiplying...
  • ProTDB

  • Referenced in 20 articles [sw13843]
  • data poses several modeling challenges: due to its structure, due to the possibility of uncertainty ... experimentally. We have used ProTDB to manage data from two application areas: protein chemistry data...
  • STAN

  • Referenced in 2 articles [sw38676]
  • waste management) under consideration of data uncertainties. The main idea behind STAN is the combination ... software product: graphical modelling, data management, calculations and graphical presentation of the results. The benefits...
  • refund

  • Referenced in 55 articles [sw07434]
  • Functional Data. Corrected confidence bands for functional data using principal components. Functional principal components ... obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous...
  • boost

  • Referenced in 40 articles [sw35655]
  • BagBoosting for tumor classification with gene expression data. Motivation: Microarray experiments are expected to contribute ... serve as a quantification of the predictive uncertainty. A very promising solution is to combine ... boosting on real and simulated gene expression data. This quasi-guaranteed improvement can be obtained...
  • FATODE

  • Referenced in 7 articles [sw11044]
  • examples include parameter estimation, data assimilation, optimal control, and uncertainty quantification...
  • iTOUGH2

  • Referenced in 5 articles [sw34111]
  • local and global sensitivity analyses, data-worth analyses, and uncertainty propagation analyses. Development of iTOUGH2...
  • MSSMOBILE

  • Referenced in 1 article [sw11704]
  • does not take into account uncertainty in the input data. The program SmartMOBILE enhances ... correct and measure the influence of data uncertainty...
  • MCDB

  • Referenced in 1 article [sw12046]
  • managing uncertain data. To deal with data uncertainty, existing probabilistic database systems augment tuples with ... loaded into the database along with the data itself. This approach can severely limit ... gracefully handle complex or unforeseen types of uncertainty, and does not permit the uncertainty model ... uncertain data that is based on a Monte Carlo approach. MCDB represents uncertainty...
  • HRMS

  • Referenced in 3 articles [sw29384]
  • useful toolkit for model averaging in big data analytics. Frequentist model averaging has been demonstrated ... tool to deal with model uncertainty in big data analysis. In contrast with a conventional...
  • trackit

  • Referenced in 1 article [sw27397]
  • precision is estimated by propagating the data uncertainties through the geolocation process. The model...
  • Vizumap

  • Referenced in 1 article [sw37579]
  • package for visualising uncertainty in spatial data. To make a sound data-driven decision ... evidence. Therefore, it is important that the uncertainty of statistical estimates be e ectively communicated ... decision-making process. Generally, this uncertainty information is shared through visualisation (e.g., error bars). However ... scientists, data journalists, etc., discussing uncertainty in spatial data...