errorprop.h File Reference

Automatic propagation of uncertainties on arbitrary functions. More...

#include "../core/core_traits.h"
#include "../algebra/vec.h"
#include "../autodiff/autodiff.h"
#include "../statistics/statistics.h"
#include "../pseudorandom/montecarlo.h"
#include "../pseudorandom/sampling.h"

Go to the source code of this file.

Namespaces
namespace	theoretica
	Main namespace of the library which contains all functions and objects.
namespace	theoretica::stats
	Statistical functions.

Functions
template<typename Matrix = mat<real>, typename Dataset = vec<real>, enable_vector< Dataset > = true>
Matrix	theoretica::stats::covar_mat (const std::vector< Dataset > &v)
	Build the covariance matrix given a vector of datasets by computing the covariance between all couples of sets.
template<unsigned int N = 0, typename MultiDualFunction = autodiff::dreal_t<N>(*)(autodiff::dvec_t<N>)>
real	theoretica::stats::propagerr (MultiDualFunction f, const vec< real, N > &x_best, const vec< real, N > &delta_x)
	Automatically propagate uncertainties under quadrature on an arbitrary function given the uncertainties on the variables, the mean values of the variables and the function itself, by using automatic differentiation.
template<unsigned int N = 0, typename Matrix , enable_matrix< Matrix > = true, typename MultiDualFunction = autodiff::dreal_t<N>(*)(autodiff::dvec_t<N>)>
real	theoretica::stats::propagerr (MultiDualFunction f, const vec< real, N > &x_best, const Matrix &cm)
	Automatically propagate uncertainties under quadrature on an arbitrary function given the uncertainties on the variables, the mean values of the variables and the function itself, using automatic differentiation to compute the gradient.
template<unsigned int N = 0, typename MultiDualFunction = multidual<N>(*)(autodiff::dvec_t<N>), typename Dataset = vec<real, N>>
real	theoretica::stats::propagerr (MultiDualFunction f, const std::vector< Dataset > &v)
	Automatically propagate uncertainties under quadrature on an arbitrary function given the function and the set of measured data.
template<typename Function >
real	theoretica::stats::propagerr_mc (Function f, std::vector< pdf_sampler > &rv, unsigned int N=1E+6)
	Propagate the statistical error on a given function using the Monte Carlo method, by generating a sample following the probability distribution of the function and computing its standard deviation.

Detailed Description

Automatic propagation of uncertainties on arbitrary functions.