mixpp: bdm::enorm< sq_T > Class Template Reference
bdm::enorm

bdm::enorm< sq_T > Class Template Reference

Gaussian density with positive definite (decomposed) covariance matrix. More...

#include <exp_family.h>

Inheritance diagram for bdm::enorm< sq_T >:

bdm::eEF bdm::epdf bdm::pdf bdm::root bdm::elognorm List of all members.

Public Member Functions

Constructors
 enorm (const vec &mu, const sq_T &R)
void set_parameters (const vec &mu, const sq_T &R)
void from_setting (const Setting &root)
void to_setting (Setting &root) const
void validate ()
Mathematical operations
void dupdate (mat &v, double nu=1.0)
 dupdate in exponential form (not really handy)
double bhattacharyya (const enorm< sq_T > &e2)
 evaluate bhattacharya distance
vec sample () const
double evallog_nn (const vec &val) const
double lognc () const
vec mean () const
vec variance () const
mat covariance () const
shared_ptr< pdf > condition (const RV &rvn) const
void condition (const RV &rvn, pdf &target) const
shared_ptr< epdf > marginal (const RV &rvn) const
void marginal (const RV &rvn, enorm< sq_T > &target) const
Access to attributes
vec & _mu ()
const vec & _mu () const
void set_mu (const vec mu0)
sq_T & _R ()
const sq_T & _R () const

Protected Attributes

vec mu
 mean value
sq_T R
 Covariance matrix in decomposed form.

Detailed Description

template<class sq_T>
class bdm::enorm< sq_T >

Gaussian density with positive definite (decomposed) covariance matrix.

More?...

Member Function Documentation

template<class sq_T>
void bdm::enorm< sq_T >::from_setting ( const Setting &  root  )  [virtual]

Create Normal density

\[ f(rv) = N(\mu, R) \]

from structure 

    class = 'enorm<ldmat>', (OR) 'enorm<chmat>', (OR) 'enorm<fsqmat>';
    mu    = [];                  // mean value
    R     = [];                  // variance, square matrix of appropriate dimension

Reimplemented from bdm::epdf.

The documentation for this class was generated from the following file:
Generated on 2 Dec 2013 for mixpp by  doxygen 1.4.7