• DREAM

  • Referenced in 9 articles [sw24746]
  • system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation ... model output, and inference of the model parameters. Bayes theorem states that the posterior probability ... models the posterior distribution is often high dimensional and analytically intractable, and sampling methods ... involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation models, formal/informal...
  • DatabionicSwarm

  • Referenced in 2 articles [sw39852]
  • adapt itself to structures of high-dimensional data such as natural clusters characterized by distance ... structures in the data space. The first module is the parameter-free projection method called ... second module is the parameter-free high-dimensional data visualization technique, which generates projected points...
  • acebayes

  • Referenced in 12 articles [sw20243]
  • typically intractable and the design space may be high-dimensional. The package implements the approximate ... utility via a sequence of conditional one-dimensional optimisation steps. At each step, a Gaussian ... designs are found for the goals of parameter estimation, model selection and prediction...
  • PyDREAM

  • Referenced in 1 article [sw34672]
  • slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python ... PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed...
  • SparseFIS

  • Referenced in 11 articles [sw13736]
  • clustering process in the input/output feature space with iterative vector quantization and projects the obtained ... third phase estimates the linear consequent parameters by a regularized sparsity-constrained-optimization procedure ... applied in order to force linear parameters to be 0, triggering a feature-selection mechanism ... evaluated, which is based on high-dimensional data from industrial processes and based on benchmark...
  • SimEnv

  • Referenced in 0 articles [sw19587]
  • dimensional output in high-dimensional model factor (parameter / initial values) spaces. Interfacing models...
  • LargeVis

  • Referenced in 6 articles [sw34905]
  • layouts the graph in the low-dimensional space. Comparing to t-SNE, LargeVis significantly reduces ... thus easily scales to millions of high-dimensional data points. Experimental results on real-world ... both efficiency and effectiveness. The hyper-parameters of LargeVis are also much more stable over...
  • MOVI

  • Referenced in 4 articles [sw08104]
  • engineering optimization problems, a method called Parameter Space Investigation (PSI method) has been created ... science, and technology (e.g., design of the space shuttle, nuclear reactor, missile, automobile, ship ... great importance while solving high-dimensional multiparameter and multicriteria problems. The PSI method is implemented...
  • flm

  • Referenced in 3 articles [sw28185]
  • Simultaneous variable selection and smoothing for high-dimensional function-on-scalar regression. We present ... separable Hilbert space. To select important predictors while also producing smooth parameter estimates, we utilize...
  • Slycat

  • Referenced in 1 article [sw24798]
  • variables, in a shared high-dimensional space describing a particular problem domain. Ensemble analysis looks ... that describe aspects of the underlying problem space. Sensitivity analysis is a type of ensemble ... that evaluates how changes in simulation input parameters correlate with simulation results. Commonly, simple regression...
  • DDMOA2

  • Referenced in 1 article [sw11882]
  • well as the set of strategy parameters. The main novelty and the primary strength ... increase the search pressure in high-dimensional objective space, we impose an additional condition...
  • GigaMesh

  • Referenced in 1 article [sw06599]
  • high-dimensional feature spaces. Convolutions and combined metrics are applied to the feature spaces ... invariants are investigated. Understanding these properties is highly relevant for robust curvature measures and segmentation ... pipeline. The pipeline has only one relevant parameter, which is the maximum size...
  • Diffpack

  • Referenced in 115 articles [sw00203]
  • As modern programming methodologies migrate from computer science...
  • LAPACK

  • Referenced in 1695 articles [sw00503]
  • LAPACK is written in Fortran 90 and provides...
  • LSQR

  • Referenced in 394 articles [sw00530]
  • Algorithm 583: LSQR: Sparse Linear Equations and Least...
  • Maple

  • Referenced in 5363 articles [sw00545]
  • The result of over 30 years of cutting...
  • MapReduce

  • Referenced in 262 articles [sw00546]
  • MapReduce is a new parallel programming model initially...
  • Matlab

  • Referenced in 13460 articles [sw00558]
  • MATLAB® is a high-level language and interactive...
  • mclust

  • Referenced in 304 articles [sw00563]
  • R package mclust: Normal Mixture Modeling for Model...