bdm::egiw Class Reference

Gauss-inverse-Wishart density stored in LD form. More...

#include <libEF.h>

List of all members.

Public Member Functions

vec sample () const

Returns a sample, $ x $ from density $ f_x()$ .

vec mean () const

return expected value

vec variance () const

return expected variance (not covariance!)

void mean_mat (mat &M, mat &R) const

double evallog_nn (const vec &val) const

In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise val = [theta_1 theta_2 ... r_1 r_2 ].

double lognc () const

logarithm of the normalizing constant, $\mathcal{I}$

void pow (double p)

Power of the density, used e.g. to flatten the density.

virtual void dupdate (mat &v)

TODO decide if it is really needed.

virtual double evallog (const vec &val) const

Evaluate normalized log-probability.

virtual vec evallog (const mat &Val) const

Evaluate normalized log-probability for many samples.

Constructors

egiw ()

egiw (int dimx0, ldmat V0, double nu0=-1.0)

void set_parameters (int dimx0, ldmat V0, double nu0=-1.0)

Access attributes

ldmat & _V ()

const ldmat & _V () const

double & _nu ()

const double & _nu () const

Constructors

Construction of each epdf should support two types of constructors:

empty constructor,

copy constructor,

The following constructors should be supported for convenience:

constructor followed by calling set_parameters()

constructor accepting random variables calling set_rv()

All internal data structures are constructed as empty. Their values (including sizes) will be set by method set_parameters(). This way references can be initialized in constructors.

void set_parameters (int dim0)

Matematical Operations

virtual mat sample_m (int N) const

Returns N samples, $[x_1 , x_2 , \ldots \$ from density $ f_x(rv)$ .

virtual vec evallog_m (const mat &Val) const

Compute log-probability of multiple values argument val.

virtual mpdf * condition (const RV &rv) const

Return conditional density on the given RV, the remaining rvs will be in conditioning.

virtual epdf * marginal (const RV &rv) const

Return marginal density on the given RV, the remainig rvs are intergrated out.

virtual void qbounds (vec &lb, vec &ub, double percentage=0.95) const

Lower and upper bounds of percentage % quantile, returns mean-2*sigma as default.

Connection to other classes

Description of the random quantity via attribute rv is optional. For operations such as sampling rv does not need to be set. However, for marginalization and conditioning rv has to be set. NB:

void set_rv (const RV &rv0)

Name its rv.

bool isnamed () const

True if rv is assigned.

const RV & _rv () const

Return name (fails when isnamed is false).

Access to attributes

int dimension () const

Size of the random variable.

Protected Attributes

ldmat V

Extended information matrix of sufficient statistics.

double nu

Number of data records (degrees of freedom) of sufficient statistics.

int dimx

Dimension of the output.

int nPsi

Dimension of the regressor.

int dim

dimension of the random variable

RV rv

Description of the random variable.

Detailed Description

Gauss-inverse-Wishart density stored in LD form.

For $p$ -variate densities, given rv.count() should be $p\times$ V.rows().

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

libEF.h
libEF.cpp


Public Member Functions
vec	sample () const
	Returns a sample, from density .
vec	mean () const
	return expected value
vec	variance () const
	return expected variance (not covariance!)
void	mean_mat (mat &M, mat &R) const
double	evallog_nn (const vec &val) const
	In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise val = [theta_1 theta_2 ... r_1 r_2 ].
double	lognc () const
	logarithm of the normalizing constant, $\mathcal{I}$
void	pow (double p)
	Power of the density, used e.g. to flatten the density.
virtual void	dupdate (mat &v)
	TODO decide if it is really needed.
virtual double	evallog (const vec &val) const
	Evaluate normalized log-probability.
virtual vec	evallog (const mat &Val) const
	Evaluate normalized log-probability for many samples.
Constructors
	egiw ()
	egiw (int dimx0, ldmat V0, double nu0=-1.0)
void	set_parameters (int dimx0, ldmat V0, double nu0=-1.0)
Access attributes
ldmat &	_V ()
const ldmat &	_V () const
double &	_nu ()
const double &	_nu () const
Constructors
Construction of each epdf should support two types of constructors: empty constructor, copy constructor, The following constructors should be supported for convenience: constructor followed by calling `set_parameters()` constructor accepting random variables calling `set_rv()` All internal data structures are constructed as empty. Their values (including sizes) will be set by method `set_parameters()`. This way references can be initialized in constructors.
void	set_parameters (int dim0)
Matematical Operations
virtual mat	sample_m (int N) const
	Returns N samples, $[x_1 , x_2 , \ldots \$ from density .
virtual vec	evallog_m (const mat &Val) const
	Compute log-probability of multiple values argument `val`.
virtual mpdf *	condition (const RV &rv) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
virtual epdf *	marginal (const RV &rv) const
	Return marginal density on the given RV, the remainig rvs are intergrated out.
virtual void	qbounds (vec &lb, vec &ub, double percentage=0.95) const
	Lower and upper bounds of `percentage` % quantile, returns mean-2*sigma as default.
Connection to other classes
Description of the random quantity via attribute `rv` is optional. For operations such as sampling `rv` does not need to be set. However, for `marginalization` and `conditioning` `rv` has to be set. NB:
void	set_rv (const RV &rv0)
	Name its rv.
bool	isnamed () const
	True if rv is assigned.
const RV &	_rv () const
	Return name (fails when isnamed is false).
Access to attributes
int	dimension () const
	Size of the random variable.
Protected Attributes
ldmat	V
	Extended information matrix of sufficient statistics.
double	nu
	Number of data records (degrees of freedom) of sufficient statistics.
int	dimx
	Dimension of the output.
int	nPsi
	Dimension of the regressor.
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.