bdm::mlognorm Class Reference

Log-Normal random walk. More...

#include <exp_family.h>

List of all members.

Public Member Functions
	mlognorm ()
	Constructor.
void	set_parameters (int size, double k)
	Set value of k.
void	condition (const vec &val)
	Update ep so that it represents this mpdf conditioned on rvc = cond.
void	from_setting (const Setting &set)
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
virtual void	validate ()
	This method TODO.
Matematical operations
virtual vec	samplecond (const vec &cond)
	Returns a sample from the density conditioned on 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.
virtual vec	evallogcond_m (const Array< vec > &Dt, const vec &cond)
	Array<vec> 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
elognorm	eno
double	sig2
	parameter 1/2*sigma^2
vec &	mu
	access
int	dimc
	dimension of the condition
RV	rvc
	random variable in condition
epdf *	ep
	pointer to internal epdf

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Detailed Description

Log-Normal random walk.

Mean value, , is...

==== Check == vv = Standard deviation of the random walk is proportional to one -th the mean. This is achieved by setting and .

The standard deviation of the walk is then: .

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Member Function Documentation

void bdm::mlognorm::from_setting

(

const Setting &

set

)

[virtual]

UI for mlognorm

The mlognorm is constructed from a structure with fields:

                        system = {
                                type = "mlognorm";
                                k = 0.1;                                  // constant k
                                mu0 = [1., 1.];
                                
                                // == OPTIONAL ==
                                // description of y variables
                                y = {type="rv"; names=["y", "u"];};
                                // description of u variable
                                u = {type="rv"; names=[];}
                        };

Reimplemented from bdm::mpdf.

References condition(), bdm::UI::get(), and set_parameters().

virtual vec bdm::mpdf::samplecond

(

const vec &

cond

)

[inline, virtual, inherited]

Returns a sample from the density conditioned on 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, .
	cond	is numeric value of rv

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

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

• exp_family.h
• exp_family.cpp