Class describing the Vavilov pdf. The probability density function of the Vavilov distribution is given by: \f[ p(\lambda; \kappa, \beta^2) = \frac{1}{2 \pi i}\int_{c-i\infty}^{c+i\infty} \phi(s) e^{\lambda s} ds\f] where \f$\phi(s) = e^{C} e^{\psi(s)}\f$ with \f$ C = \kappa (1+\beta^2 \gamma )\f$ and \f$\psi(s)&=& s \ln \kappa + (s+\beta^2 \kappa) \cdot \left ( \int \limits_{0}^{1} \frac{1 - e^{\frac{-st}{\kappa}}}{t} \,\der t- \gamma \right ) - \kappa \, e^{\frac{-s}{\kappa}}\f$. \f$ \gamma = 0.5772156649\dots\f$ is Euler's constant. The parameters are: - 0: Norm: Normalization constant - 1: x0: Location parameter - 2: xi: Width parameter - 3: kappa: Parameter \f$\kappa\f$ of the Vavilov distribution - 4: beta2: Parameter \f$\beta^2\f$ of the Vavilov distribution Benno List, June 2010 @ingroup StatFunc
virtual | ~VavilovAccuratePdf() |
virtual ROOT::Math::IBaseFunctionOneDim* | Clone() const |
virtual double | DoEval(double x) const |
virtual double | DoEvalPar(double x, const double* p) const |
virtual unsigned int | NPar() const |
double | ROOT::Math::IParametricFunctionOneDim::operator()(double x, const double* p) const |
double | ROOT::Math::IParametricFunctionOneDim::operator()(const double* x, const double* p) const |
ROOT::Math::VavilovAccuratePdf& | operator=(const ROOT::Math::VavilovAccuratePdf&) |
virtual string | ParameterName(unsigned int i) const |
virtual const double* | Parameters() const |
virtual void | SetParameters(const double* p) |
ROOT::Math::VavilovAccuratePdf | VavilovAccuratePdf() |
ROOT::Math::VavilovAccuratePdf | VavilovAccuratePdf(const double* p) |
ROOT::Math::VavilovAccuratePdf | VavilovAccuratePdf(const ROOT::Math::VavilovAccuratePdf&) |
Constructor with parameter values @param p vector of doubles containing the parameter values (Norm, x0, xi, kappa, beta2).
Set the parameter values @param p vector of doubles containing the parameter values (Norm, x0, xi, kappa, beta2).
Return the name of the i-th parameter (starting from zero)
Evaluate the function @param x The Landau parameter \f$x = \lambda_L\f$
Evaluate the function, using parameters p @param x The Landau parameter \f$x = \lambda_L\f$ @param p vector of doubles containing the parameter values (Norm, x0, xi, kappa, beta2).