bdm::enorm< sq_T > Class Template Reference
Gaussian density with positive definite (decomposed) covariance matrix. More...
#include <exp_family.h>
Public Member Functions | |
| 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 | |
| enorm () | |
| enorm (const vec &mu, const sq_T &R) | |
| void | set_parameters (const vec &mu, const sq_T &R) |
| void | from_setting (const Setting &root) |
| void | validate () |
| This method TODO. | |
Mathematical operations | |
| void | dupdate (mat &v, double nu=1.0) |
| dupdate in exponential form (not really handy) | |
| vec | sample () const |
Returns a sample,  from density  . | |
| double | evallog_nn (const vec &val) const |
| Evaluate normalized log-probability. | |
| double | lognc () const |
logarithm of the normalizing constant,  | |
| vec | mean () const |
| return expected value | |
| 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< sq_T > &target) const |
Access to attributes | |
| vec & | _mu () |
| const vec & | _mu () const |
| void | set_mu (const vec mu0) |
| sq_T & | _R () |
| const sq_T & | _R () const |
Constructors | |
Construction of each epdf should support two types of constructors:
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,  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 | |
| sq_T | R |
| Covariance matrix in decomposed form. | |
| int | dim |
| dimension of the random variable | |
| RV | rv |
| Description of the random variable. | |
Detailed Description
template<class sq_T>
class bdm::enorm< sq_T >
Gaussian density with positive definite (decomposed) covariance matrix.
More?...
Member Function Documentation
template<class sq_T >
| void bdm::enorm< sq_T >::from_setting | ( | const Setting & | root | ) | [inline, virtual] |
Create Normal density
![\[ f(rv) = N(\mu, R) \]](form_164.png)
from structure
class = 'enorm<ldmat>', (OR) 'enorm<chmat>', (OR) 'enorm<fsqmat>'; mu = []; // mean value R = []; // variance, square matrix of appropriate dimension
Reimplemented from bdm::epdf.
References bdm::UI::get(), bdm::enorm< sq_T >::mu, bdm::enorm< sq_T >::R, and bdm::enorm< sq_T >::validate().
The documentation for this class was generated from the following file:
