bdm::eigamma Class Reference

Inverse-Gamma posterior density. More...

#include <exp_family.h>

List of all members.

Public Member Functions

double evallog (const vec &val) const

Evaluate normalized log-probability.

double lognc () const

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

vec & _alpha ()

Returns pointer to internal alpha. Potentially dengerous: use with care!

vec & _beta ()

Returns pointer to internal beta. Potentially dengerous: use with care!

void from_setting (const Setting &set)

void validate ()

This method TODO.

virtual double evallog_nn (const vec &val) const

Evaluate normalized log-probability.

virtual vec evallog_m (const mat &Val) const

Evaluate normalized log-probability for many samples.

virtual vec evallog_m (const Array< vec > &Val) const

Evaluate normalized log-probability for many samples.

virtual void pow (double p)

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

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.

All constructors are inherited

vec sample () const

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

vec mean () const

Returns poiter to alpha and beta. Potentially dangerous: use with care!

vec variance () const

return expected variance (not covariance!)

Constructors

void set_parameters (const vec &a, const vec &b)

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 shared_ptr< mpdf > condition (const RV &rv) const

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

virtual shared_ptr< 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

vec alpha

Vector $\alpha$ .

vec beta

Vector $\beta$ .

int dim

dimension of the random variable

RV rv

Description of the random variable.

Detailed Description

Inverse-Gamma posterior density.

Multivariate inverse-Gamma density as product of independent univariate densities.

$f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i)$

Vector $\beta$ has different meaning (in fact it is 1/beta as used in definition of iG)

Inverse Gamma can be converted to Gamma using

$x\sim iG(a,b) => 1/x\sim G(a,1/b)$

This relation is used in sampling.

Member Function Documentation

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

Create Gamma density

$f(rv|rvc) = \Gamma(\alpha, \beta)$

from structure

                class = 'egamma';
                alpha = [...];         // vector of alpha
                beta = [...];          // vector of beta
                rv = RV({'name'})      // description of RV

Reimplemented from bdm::epdf.

References bdm::egamma::alpha, bdm::UI::get(), and bdm::egamma::validate().

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

exp_family.h


Public Member Functions
double	evallog (const vec &val) const
	Evaluate normalized log-probability.
double	lognc () const
	logarithm of the normalizing constant, $\mathcal{I}$
vec &	_alpha ()
	Returns pointer to internal alpha. Potentially dengerous: use with care!
vec &	_beta ()
	Returns pointer to internal beta. Potentially dengerous: use with care!
void	from_setting (const Setting &set)
void	validate ()
	This method TODO.
virtual double	evallog_nn (const vec &val) const
	Evaluate normalized log-probability.
virtual vec	evallog_m (const mat &Val) const
	Evaluate normalized log-probability for many samples.
virtual vec	evallog_m (const Array< vec > &Val) const
	Evaluate normalized log-probability for many samples.
virtual void	pow (double p)
	Power of the density, used e.g. to flatten the density.
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.

All constructors are inherited
vec	sample () const
	Returns a sample, from density .
vec	mean () const
	Returns poiter to alpha and beta. Potentially dangerous: use with care!
vec	variance () const
	return expected variance (not covariance!)
Constructors
void	set_parameters (const vec &a, const vec &b)
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 shared_ptr< mpdf >	condition (const RV &rv) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
virtual shared_ptr< 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
vec	alpha
	Vector $\alpha$ .
vec	beta
	Vector $\beta$ .
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.