bdm::mlnorm< sq_T > Class Template Reference

Normal distributed linear function with linear function of mean value;. More...

#include <libEF.h>

List of all members.

Public Member Functions

void condition (const vec &cond)
vec & _mu_const ()
 access function
mat & _A ()
 access function
mat _R ()
 access function
Constructors
 mlnorm ()
 mlnorm (const mat &A, const vec &mu0, const sq_T &R)
void set_parameters (const mat &A, const vec &mu0, const sq_T &R)
 Set A and R.
Matematical operations
virtual vec samplecond (const vec &cond)
 Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.
virtual mat samplecond_m (const vec &cond, int N)
 Returns.
virtual double evallogcond (const vec &dt, const vec &cond)
 Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently.
virtual vec evallogcond_m (const mat &Dt, const vec &cond)
 Matrix version of evallogcond.
Access to attributes
RV _rv ()
RV _rvc ()
int dimension ()
int dimensionc ()
epdf_epdf ()
epdf_e ()
Connection to other objects
void set_rvc (const RV &rvc0)
void set_rv (const RV &rv0)
bool isnamed ()

Protected Attributes

enorm< sq_T > epdf
 Internal epdf that arise by conditioning on rvc.
mat A
vec mu_const
vec & _mu
int dimc
 dimension of the condition
RV rvc
 random variable in condition
epdfep
 pointer to internal epdf

Friends

template<class sq_M >
std::ostream & operator<< (std::ostream &os, mlnorm< sq_M > &ml)


Detailed Description

template<class sq_T>
class bdm::mlnorm< sq_T >

Normal distributed linear function with linear function of mean value;.

Mean value $mu=A*rvc+mu_0$.


Member Function Documentation

template<class sq_T >
void bdm::mlnorm< sq_T >::condition ( const vec &  cond  )  [inline, virtual]

Set value of rvc . Result of this operation is stored in epdf use function _ep to access it.

Reimplemented from bdm::mpdf.

Reimplemented in bdm::mlstudent.

virtual vec bdm::mpdf::samplecond ( const vec &  cond  )  [inline, virtual, inherited]

Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.

Parameters:
cond is numeric value of rv

Reimplemented in bdm::mprod.

References bdm::mpdf::condition(), bdm::mpdf::ep, and bdm::epdf::sample().

Referenced by bdm::MPF< BM_T >::bayes(), bdm::PF::bayes(), and bdm::ArxDS::step().

virtual mat bdm::mpdf::samplecond_m ( const vec &  cond,
int  N 
) [inline, virtual, inherited]

Returns.

Parameters:
N samples from the density conditioned on cond, $x \sim epdf(rv|cond)$.
cond is numeric value of rv

References bdm::mpdf::condition(), bdm::epdf::dimension(), bdm::mpdf::ep, and bdm::epdf::sample().


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

Generated on Thu Apr 23 21:06:46 2009 for mixpp by  doxygen 1.5.8