24 #ifndef LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED 25 #define LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED 30 namespace lsst {
namespace meas {
namespace modelfit {
36 "Width of exponential ellipticity distribution (conformal shear units)." 41 "Softened core width for ellipticity distribution (conformal shear units)." 51 "ln(radius) at which the softened cutoff begins towards the minimum" 56 "Mean of the Student's T distribution used for ln(radius) at large radius, and the transition " 57 "point between a flat distribution and the Student's T." 62 "Width of the Student's T distribution in ln(radius)." 67 "Number of degrees of freedom for the Student's T distribution on ln(radius)." 93 ndarray::Array<Scalar const,1,1>
const & nonlinear,
94 ndarray::Array<Scalar const,1,1>
const & amplitudes
98 void evaluateDerivatives(
99 ndarray::Array<Scalar const,1,1>
const & nonlinear,
100 ndarray::Array<Scalar const,1,1>
const & amplitudes,
101 ndarray::Array<Scalar,1,1>
const & nonlinearGradient,
102 ndarray::Array<Scalar,1,1>
const & amplitudeGradient,
103 ndarray::Array<Scalar,2,1>
const & nonlinearHessian,
104 ndarray::Array<Scalar,2,1>
const & amplitudeHessian,
105 ndarray::Array<Scalar,2,1>
const & crossHessian
111 ndarray::Array<Scalar const,1,1>
const & nonlinear
117 ndarray::Array<Scalar const,1,1>
const & nonlinear,
118 ndarray::Array<Scalar,1,1>
const & amplitudes
124 ndarray::Array<Scalar const,1,1>
const & nonlinear,
126 ndarray::Array<Scalar,2,1>
const & amplitudes,
127 ndarray::Array<Scalar,1,1>
const & weights,
128 bool multiplyWeights=
false 140 #endif // !LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED A piecewise prior motivated by both real distributions and practical considerations.
SemiEmpiricalPriorControl()
double Scalar
Typedefs to be used for probability and parameter values.
double logRadiusMu
"Mean of the Student's T distribution used for ln(radius) at large radius, and the transition " "poin...
#define LSST_CONTROL_FIELD(NAME, TYPE, DOC)
double logRadiusSigma
"Width of the Student's T distribution in ln(radius)." ;
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
Typedefs to be used for probability and parameter values.
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
Typedefs to be used for probability and parameter values.
void validate() const
Raise InvalidParameterException if the configuration options are invalid.
Base class for Bayesian priors.
double logRadiusNu
"Number of degrees of freedom for the Student's T distribution on ln(radius)." ;
double ellipticityCore
"Softened core width for ellipticity distribution (conformal shear units)." ;
double ellipticitySigma
"Width of exponential ellipticity distribution (conformal shear units)." ;
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
double logRadiusMinOuter
"Minimum ln(radius)." ;
SemiEmpiricalPriorControl Control