Documentation / Developer's manual
Available modules
Calibration,  DataServer,  Launcher,  MetaModelOptim,  Modeler,  Optimizer,  ReLauncher,  Reliability,  ReOptimizer,  Sampler,  Sensitivity,  UncertModeler,  XmlProblem,  ![]() |
Uranie / Calibration
v4.11.0
|
Classes | |
| class | TABC |
| class | TCalibration |
| Description of the class TCalibration. More... | |
| class | TCirce |
| class | TDistanceLikelihoodFunction |
| Description of the class TDistanceLikelihoodFunction. More... | |
| class | TGaussLogLikelihoodFunction |
| Description of the class TGaussLogLikelihoodFunction The log-likelihood is estimated as \[ \textit{log-}\mathcal{L} \left(\theta | \mathbf{x},\mathbf{y}\right) = -\frac{1}{2}\sum_{j=1}^{n_{Var}}\sum_{i=1}^{n_{Obs}}\left( \log\left( 2 \pi \left(\sigma_i^j\right)^2 \right)+\left(\frac{\mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}}}{\sigma_i^j}\right)^2\right) \] where. More... | |
| class | TL1DistanceFunction |
| Description of the class TL1DistanceFunction The distance is estimated as \[ Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times ( \sum_{j=1}^{n_{Obs}} | \mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}} | ) \] where. More... | |
| class | TLinearBayesian |
| Description of the class TLinearBayesian. More... | |
| class | TLSDistanceFunction |
| Description of the class TLSDistanceFunction The distance is estimated as \[ Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times \sqrt{ \sum_{j=1}^{n_{Obs}} (\mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}})^{2}} \] where. More... | |
| class | TMahalanobisDistanceFunction |
| Description of the class TMahalanobisDistanceFunction The distance is estimated as \[ Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times \sqrt{ (\mathbf{y_{i,Obs}} - \mathbf{y_{i,Est}})^{T} \Omega^{-1} (\mathbf{y_{i,Obs}} - \mathbf{y_{i,Est}}) } \] where. More... | |
| class | TMCMC |
| class | TMinimisation |
| Description of the class TMinimisation. More... | |
| class | TPMCABC |
| class | TRejectionABC |
| class | TRelativeLSDistanceFunction |
| Description of the class TRelativeLSDistanceFunction The distance is estimated as \[ Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times \sqrt{ \sum_{j=1}^{n_{Obs}} \frac{(\mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}})^{2}}{(\mathbf{y^{j}_{i,Obs}})^{2}}} \] where. More... | |
| class | TUncertModeler |
| class | TWeightedLSDistanceFunction |
| Description of the class TWeightedLSDistanceFunction The distance is estimated as \[ Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times \sqrt{ \sum_{j=1}^{n_{Obs}} \beta_{j} (\mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}})^{2}} \] where. More... | |
