enorm< sq_T > Class Template Reference

Gaussian density with positive definite (decomposed) covariance matrix. More...

#include <libEF.h>

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List of all members.

Public Member Functions

 enorm (RV &rv)
 Default constructor.
void set_parameters (const vec &mu, const sq_T &R)
 Set mean value mu and covariance R.
void tupdate (double phi, mat &vbar, double nubar)
 tupdate in exponential form (not really handy)
void dupdate (mat &v, double nu=1.0)
 dupdate in exponential form (not really handy)
vec sample () const
 Returns a sample, $x$ from density $epdf(rv)$.
mat sample (int N) const
 TODO is it used?
double eval (const vec &val) const
 Compute probability of argument val.
double evalpdflog (const vec &val) const
 Evaluate normalized log-probability.
double lognc () const
 logarithm of the normalizing constant, $\mathcal{I}$
vec mean () const
 return expected value
vec & _mu ()
 returns a pointer to the internal mean value. Use with Care!
void set_mu (const vec mu0)
 access function
sq_T & _R ()
 returns pointers to the internal variance and its inverse. Use with Care!
mat getR ()
 access method
virtual void dupdate (mat &v)
 TODO decide if it is really needed.
virtual double evalpdflog_nn (const vec &val) const
 Evaluate normalized log-probability.
virtual vec evalpdflog (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 mat sampleN (int N) const
 Returns N samples from density $epdf(rv)$.
const RV_rv () const
 access function, possibly dangerous!
void _renewrv (const RV &in_rv)
 modifier function - useful when copying epdfs

Protected Attributes

vec mu
 mean value
sq_T R
 Covariance matrix in decomposed form.
int dim
 dimension (redundant from rv.count() for easier coding )
RV rv
 Identified of the random variable.


Detailed Description

template<class sq_T>
class enorm< sq_T >

Gaussian density with positive definite (decomposed) covariance matrix.

More?...


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

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