bdm::elognorm Class Reference

#include <exp_family.h>

Public Member Functions
vec	sample () const
	Returns a sample, from density .
vec	mean () const
	return expected value
virtual double	evallog (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.
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!)
shared_ptr< mpdf >	condition (const RV &rvn) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
void	condition (const RV &rvn, mpdf &target) const
shared_ptr< epdf >	marginal (const RV &rvn) const
	Return marginal density on the given RV, the remainig rvs are intergrated out.
void	marginal (const RV &rvn, enorm< ldmat > &target) const
Access to attributes

vec &	_mu ()
const vec &	_mu () const
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 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

bdm::elognorm Class Reference

Public Member Functions

Protected Attributes

Detailed Description

Member Function Documentation