bdm::eWishartCh Class Reference

#include <exp_family.h>

List of all members.

Public Member Functions

void set_parameters (const mat &Y0, const double delta0)

mat sample_mat () const

vec sample () const

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

void setY (const mat &Ch0)

fast access function y0 will be copied into Y.Ch.

void _setY (const vec &ch0)

fast access function y0 will be copied into Y.Ch.

const chmat & getY () const

access function

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.

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 double evallog (const vec &val) const

Compute log-probability of argument val.

virtual vec evallog_m (const mat &Val) const

Compute log-probability of multiple values argument val.

virtual vec evallog_m (const Array< vec > &Avec) 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 vec mean () const

return expected value

virtual vec variance () const

return expected variance (not covariance!)

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

chmat Y

Upper-Triagle of Choleski decomposition of $\Psi$ .

int p

dimension of matrix $\Psi$

double delta

degrees of freedom $\nu$

int dim

dimension of the random variable

RV rv

Description of the random variable.

Detailed Description

inverse Wishart density defined on Choleski decomposition

Member Function Documentation

void bdm::epdf::from_setting ( const Setting & set ) [inline, virtual, inherited]

Load from structure with elements:

 { rv = {class="RV", names=(...),}; // RV describing meaning of random variable
   // elements of offsprings
 }

Reimplemented from bdm::root.

Reimplemented in bdm::enorm< sq_T >, bdm::egiw, bdm::egamma, bdm::euni, bdm::merger_base, bdm::enorm< ldmat >, bdm::enorm< chmat >, and bdm::enorm< fsqmat >.

References bdm::epdf::set_rv().

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

exp_family.h


Public Member Functions
void	set_parameters (const mat &Y0, const double delta0)
mat	sample_mat () const
vec	sample () const
	Returns a sample, from density .
void	setY (const mat &Ch0)
	fast access function y0 will be copied into Y.Ch.
void	_setY (const vec &ch0)
	fast access function y0 will be copied into Y.Ch.
const chmat &	getY () const
	access function
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.
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 double	evallog (const vec &val) const
	Compute log-probability of argument `val`.
virtual vec	evallog_m (const mat &Val) const
	Compute log-probability of multiple values argument `val`.
virtual vec	evallog_m (const Array< vec > &Avec) 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 vec	mean () const
	return expected value
virtual vec	variance () const
	return expected variance (not covariance!)
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
chmat	Y
	Upper-Triagle of Choleski decomposition of $\Psi$ .
int	p
	dimension of matrix $\Psi$
double	delta
	degrees of freedom $\nu$
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.