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, .
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< ldmat >	epdf
	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
epdf *	ep
	pointer to internal epdf
Friends
std::ostream &	operator<< (std::ostream &os, mlnorm< sq_M > &ml)

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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?

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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, .

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, .
	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().

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

• libEF.h