bdm::mlstudent Class Reference

#include <libEF.h>

Inheritance diagram for bdm::mlstudent:

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Collaboration diagram for bdm::mlstudent:

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List of all members.

Public Member Functions

 mlstudent (const RV &rv0, const RV &rvc0)
void set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat &Lambda0)
void condition (const vec &cond)
 Set value of rvc . Result of this operation is stored in epdf use function _ep to access it.
void set_parameters (const mat &A, const vec &mu0, const ldmat &R)
 Set A and R.
vec & _mu_const ()
 access function
mat & _A ()
 access function
mat _R ()
 access function
virtual vec samplecond (const vec &cond, double &ll)
 Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.
virtual mat samplecond_m (const vec &cond, vec &ll, 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.
RV _rvc () const
 access function
RV _rv () const
 access function
epdf_epdf ()
 access function
epdf_e ()
 access function

Protected Attributes

ldmat Lambda
ldmat_R
ldmat Re
enorm< ldmatepdf
 Internal epdf that arise by conditioning on rvc.
mat A
vec mu_const
vec & _mu
RV rv
 modeled random variable
RV rvc
 random variable in condition
epdfep
 pointer to internal epdf

Friends

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


Detailed Description

(Approximate) Student t density with linear function of mean value

The internal epdf of this class is of the type of a Gaussian (enorm). However, each conditioning is trying to assure the best possible approximation by taking into account the zeta function. See [] for reference.

Perhaps a moment-matching technique?


Member Function Documentation

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

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

Parameters:
cond is numeric value of rv
ll is a return value of log-likelihood of the sample.

Reimplemented in bdm::mprod.

References bdm::mpdf::condition(), bdm::mpdf::ep, bdm::epdf::evallog(), 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,
vec &  ll,
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
ll is a return value of log-likelihood of the sample.

References bdm::mpdf::condition(), bdm::RV::count(), bdm::mpdf::ep, bdm::epdf::evallog(), bdm::mpdf::rv, and bdm::epdf::sample().


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

Generated on Fri Feb 6 19:50:14 2009 for mixpp by  doxygen 1.5.6