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Hyperiso 1.0.3
Modular flavour-physics calculations, Wilson coefficients and statistical inference
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Complete output of a Monte Carlo propagation run. More...
#include <MCEngine.h>

Public Attributes | |
| MCRealization | mc_real |
| std::vector< GaussianSummary > | summary |
| MCObservableCovariance | covariance |
| ObsSample = Dict[BinnedObservableId, float] | |
| ObsSamples = List[ObsSample] | |
| NuisanceSample = Dict[ParamId, float] | |
| NuisanceSamples = List[NuisanceSample] | |
Complete output of a Monte Carlo propagation run.
Definition at line 151 of file MCEngine.h.
| MCObservableCovariance MCResult::covariance |
Empirical observable covariance matrix and inverse.
Definition at line 159 of file MCEngine.h.
| MCRealization MCResult::mc_real |
Raw accepted Monte Carlo predictions and nuisance samples.
Definition at line 153 of file MCEngine.h.
| MCResult.NuisanceSample = Dict[ParamId, float] |
Definition at line 27 of file MCResult.py.
| MCResult.NuisanceSamples = List[NuisanceSample] |
Definition at line 30 of file MCResult.py.
| MCResult.ObsSample = Dict[BinnedObservableId, float] |
Definition at line 21 of file MCResult.py.
| MCResult.ObsSamples = List[ObsSample] |
Definition at line 24 of file MCResult.py.
| std::vector<GaussianSummary> MCResult::summary |
Gaussian or split-Gaussian summary of each observable distribution.
Definition at line 156 of file MCEngine.h.