• Robust Control Toolbox

  • Referenced in 144 articles [sw07907]
  • analyze the impact of plant model uncertainty on control system performance and identify worst-case ... controller against a set of plant models. You can also tune gain-scheduled controllers...
  • LFR-toolbox

  • Referenced in 26 articles [sw04756]
  • Matlab for building LFT-based uncertainty models and for LFT-based gain scheduling. A major ... large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric...
  • BioBayes

  • Referenced in 24 articles [sw08082]
  • several levels of uncertainty involved in the mathematical modelling of biochemical systems. There often ... uncertainty about the values of kinetic parameters, about the general structure of the model...
  • BLOG

  • Referenced in 41 articles [sw22025]
  • defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends ... Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first...
  • CORO

  • Referenced in 23 articles [sw02197]
  • distribution optimization under uncertainty. The authors present a modeling framework for the optimization ... Transformation and Distribution (STD) scheduling problem under uncertainty on the product demand, spot supply cost ... given scenarios. Novel schemes are presented for modeling multiperiod linking constraints, such that they...
  • refund

  • Referenced in 51 articles [sw07434]
  • uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model ... decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution ... bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method...
  • blasso

  • Referenced in 13 articles [sw06769]
  • infinite mixture of elastic net regression models that allows for adaptive, data-based shrinkage ... Markov chain Monte Carlo (MCMC) methods. Uncertainty about model specification is addressed from a Bayesian...
  • FLINTSTONES

  • Referenced in 61 articles [sw17950]
  • tuple linguistic model. Computing with words in decision making. This book examines ... wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing ... fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing...
  • QUESO

  • Referenced in 22 articles [sw10555]
  • constructs supporting research into the uncertainty quantification (UQ) of models and their predictions...
  • TrueSkill

  • Referenced in 21 articles [sw21717]
  • system tracks the uncertainty about player skills, explicitly models draws, can deal with any number...
  • DAKOTA

  • Referenced in 64 articles [sw05202]
  • mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement ... design and performance analysis of computational models on high performance computers...
  • PySP

  • Referenced in 18 articles [sw04921]
  • powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its wide ... difficulty of solving stochastic programming models, particularly in the mixed-integer, non-linear, and/or multi...
  • ADMB

  • Referenced in 13 articles [sw07416]
  • dimensional integrals for use in latent variable models. We also review the literature in which ... stability and built-in methods to quantify uncertainty...
  • DUE

  • Referenced in 3 articles [sw27796]
  • cover). Once data are imported, an uncertainty model can be developed for the positional ... attribute uncertainties of environmental objects. This is currently limited to probability models, but confidence intervals ... spatial and temporal patterns of uncertainty (autocorrelation), as well as cross-correlations between related inputs ... uncertainty methods to develop realistic uncertainty models for their data...
  • jFuzzyIBATranslator

  • Referenced in 3 articles [sw22488]
  • software tool for uncertainty modeling using Interpolative Boolean algebra. In this paper, we present ... mostly used in performance measuring and uncertainty modeling. IBA is based on the principle...
  • CreditRisk+

  • Referenced in 41 articles [sw31697]
  • causes of market price movements. The CREDITRISK+ Model considers default rates as continuous random variables ... default rates in order to capture the uncertainty in the level of default rates. Often ... background factors are incorporated into the CREDITRISK+ Model through the use of default rate volatilities...
  • MUCM

  • Referenced in 3 articles [sw13257]
  • Using emulators to estimate uncertainty in complex models. The managing uncertainty in complex model projects ... been developing methods for estimating uncertainty in complex models using emulators. Emulators are statistical descriptions ... beliefs about the models (or simulators). They can also be thought of as interpolators ... simulator runs, for example Monte Carlo uncertainty calculations. par Both Gaussian and Bayes linear emulators...
  • Pi4U

  • Referenced in 7 articles [sw18320]
  • performance computing framework for Bayesian uncertainty quantification of complex models. We present Pi4U, an extensible ... Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that...
  • ProTDB

  • Referenced in 20 articles [sw13843]
  • several modeling challenges: due to its structure, due to the possibility of uncertainty association ... elements. We present a probabilistic XML model that addresses all of these challenges. We devise...
  • PSQL

  • Referenced in 8 articles [sw02180]
  • great range of advantages over other data models, it was designed to support deterministic data ... data. However, in all real-world environments, uncertainty in data values is a common occurrence ... need to extend the relational model so that data uncertainty can be captured explicitly...