bdm::elognorm Class Reference

#include <exp_family.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

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.

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.

Constructors

void set_parameters (const vec &mu, const ldmat &R)

void from_setting (const Setting &root)

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)

Mathematical operations

void dupdate (mat &v, double nu=1.0)

dupdate in exponential form (not really handy)

double evallog_nn (const vec &val) const

Evaluate normalized log-probability.

double lognc () const

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

vec variance () const

return expected variance (not covariance!)

mpdf * condition (const RV &rvn) const

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

enorm< ldmat > * marginal (const RV &rv) const

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

Access to attributes

vec & _mu ()

void set_mu (const vec mu0)

ldmat & _R ()

const ldmat & _R () const

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

Compute log-probability of multiple values argument val.

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 mu

mean value

ldmat R

Covariance matrix in decomposed form.

int dim

dimension of the random variable

RV rv

Description of the random variable.

Detailed Description

Log-Normal probability density only allow diagonal covariances!

Density of the form $\log(x)\sim \mathcal{N}(\mu,\sigma^2)$ , i.e.

$x \sim \frac{1}{x\sigma\sqrt{2\pi}}\exp{-\frac{1}{2\sigma^2}(\log(x)-\mu)}$

Member Function Documentation

void bdm::enorm< ldmat >::from_setting ( const Setting & set ) [virtual, inherited]

Load from structure with elements:

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

Reimplemented from bdm::epdf.

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

exp_family.h


Public Member Functions
vec	sample () const
	Returns a sample, from density .
vec	mean () const
	return expected value
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.
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.
Constructors
void	set_parameters (const vec &mu, const ldmat &R)
void	from_setting (const Setting &root)
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)
Mathematical operations
void	dupdate (mat &v, double nu=1.0)
	dupdate in exponential form (not really handy)
double	evallog_nn (const vec &val) const
	Evaluate normalized log-probability.
double	lognc () const
	logarithm of the normalizing constant, $\mathcal{I}$
vec	variance () const
	return expected variance (not covariance!)
mpdf *	condition (const RV &rvn) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
enorm< ldmat > *	marginal (const RV &rv) const
	Return marginal density on the given RV, the remainig rvs are intergrated out.
Access to attributes
vec &	_mu ()
void	set_mu (const vec mu0)
ldmat &	_R ()
const ldmat &	_R () const
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 vec	evallog_m (const Array< vec > &Avec) const
	Compute log-probability of multiple values argument `val`.
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	mu
	mean value
ldmat	R
	Covariance matrix in decomposed form.
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.