bdm::migamma Class Reference

Inverse-Gamma random walk. More...

#include <libEF.h>

List of all members.

Public Member Functions

void set_parameters (int len, double k0)

Set value of k.

void condition (const vec &val)

Update ep so that it represents this mpdf conditioned on rvc = cond.

Constructors

migamma ()

migamma (const migamma &m)

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

eigamma epdf

Internal epdf that arise by conditioning on rvc.

double k

Constant $k$ .

vec & _alpha

cache of epdf.alpha

vec & _beta

cache of epdf.beta

int dimc

dimension of the condition

RV rvc

random variable in condition

epdf * ep

pointer to internal epdf

Detailed Description

Inverse-Gamma random walk.

Mean value, $\mu$ , of this density is given by rvc . Standard deviation of the random walk is proportional to one $ k $ -th the mean. This is achieved by setting $\alpha=\mu/k^2+2$ and $\beta=\mu(\alpha-1)$ .

The standard deviation of the walk is then: $\mu/\sqrt(k)$ .

Member Function Documentation

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:

libEF.h


Public Member Functions
void	set_parameters (int len, double k0)
	Set value of `k`.
void	condition (const vec &val)
	Update `ep` so that it represents this mpdf conditioned on `rvc` = cond.
Constructors
	migamma ()
	migamma (const migamma &m)
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
eigamma	epdf
	Internal epdf that arise by conditioning on `rvc`.
double	k
	Constant .
vec &	_alpha
	cache of epdf.alpha
vec &	_beta
	cache of epdf.beta
int	dimc
	dimension of the condition
RV	rvc
	random variable in condition
epdf *	ep
	pointer to internal epdf