CMP++: Uncertainty Quantification & Bayesian Calibration
Loading...
Searching...
No Matches
cmp::mean::Mean Class Referenceabstract

Abstract base class for Gaussian Process prior mean functions. More...

#include <mean++.h>

Inheritance diagram for cmp::mean::Mean:

Public Member Functions

virtual ~Mean ()=default
 
virtual double eval (const Eigen::VectorXd &x, const Eigen::VectorXd &par) const =0
 
virtual double evalGradient (const Eigen::VectorXd &x, const Eigen::VectorXd &par, const size_t &i) const =0
 
virtual double evalHessian (const Eigen::VectorXd &x, const Eigen::VectorXd &par, const size_t &i, const size_t &j) const =0
 

Detailed Description

Abstract base class for Gaussian Process prior mean functions.

Mathematical Formulation The mean function \(m: \mathbb{R}^D \to \mathbb{R}\) defines the expected value of the GP prior at any input \(\mathbf{x}\):

\[ m(\mathbf{x}) = \mathbb{E}[f(\mathbf{x})] \]

Commonly configured as a constant \(m(\mathbf{x}) = c\) or zero \(m(\mathbf{x}) = 0\).

Implementation Algorithm Provides virtual interfaces for mean evaluations (eval), first-order gradients (evalGradient) with respect to a parameter \(p_i\), and second-order Hessians (evalHessian).

Constructor & Destructor Documentation

◆ ~Mean()

virtual cmp::mean::Mean::~Mean ( )
virtualdefault

Member Function Documentation

◆ eval()

virtual double cmp::mean::Mean::eval ( const Eigen::VectorXd &  x,
const Eigen::VectorXd &  par 
) const
pure virtual

◆ evalGradient()

virtual double cmp::mean::Mean::evalGradient ( const Eigen::VectorXd &  x,
const Eigen::VectorXd &  par,
const size_t &  i 
) const
pure virtual

◆ evalHessian()

virtual double cmp::mean::Mean::evalHessian ( const Eigen::VectorXd &  x,
const Eigen::VectorXd &  par,
const size_t &  i,
const size_t &  j 
) const
pure virtual

The documentation for this class was generated from the following file: