Theory

The theoretical documentation. This contains an in-depth description of all algorithms.

• Data analysis
  • Comparison of two samples
  • Estimation of a parametric model
  • Calibration
  • Analysis of the goodness of fit of a parametric model
  • Detection and quantification of dependencies
  • Estimating a quantile
• Probabilistic modeling
  • Standard parametric models
  • Copulas
  • Random Mixture: affine combination of independent univariate distributions
  • Stochastic process
• Meta modeling
  • Linear and Quadratic Taylor Expansions
  • Least squares polynomial response surface
  • Sparse least squares polynomial metamodel
  • Kriging
  • Functional chaos
• Reliability, sensitivity
  • Central dispersion
  • Sensitivity analysis
  • Probability of exceeding a threshold
• Numerical methods
  • Uniform Random Generator
  • Distribution realizations
  • Sphere sampling method
  • Optimization Algorithms
  • Least squares problems numerical methods
  • Isoprobabilistic transformation