#include <libEF.h>


Public Member Functions | |
| enorm (const RV &rv) | |
| Default constructor.  | |
| void | set_parameters (const vec &mu, const sq_T &R) | 
Set mean value mu and covariance R.  | |
| void | dupdate (mat &v, double nu=1.0) | 
| dupdate in exponential form (not really handy)  | |
| vec | sample () const | 
Returns a sample,   from density  .  | |
| mat | sample (int N) const | 
| TODO is it used?  | |
| double | eval (const vec &val) const | 
| double | evalpdflog_nn (const vec &val) const | 
| Evaluate normalized log-probability.  | |
| double | lognc () const | 
logarithm of the normalizing constant,    | |
| vec | mean () const | 
| return expected value  | |
| mpdf * | condition (const RV &rvn) const | 
| Return conditional density on the given RV, the remaining rvs will be in conditioning.  | |
| epdf * | marginal (const RV &rv) const | 
| Return marginal density on the given RV, the remainig rvs are intergrated out.  | |
| 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!  | |
| virtual void | dupdate (mat &v) | 
| TODO decide if it is really needed.  | |
| virtual double | evalpdflog (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 | sample_m (int N) const | 
Returns N samples from density  .  | |
| virtual vec | evalpdflog_m (const mat &Val) const | 
Compute log-probability of multiple values argument val.  | |
| 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.  | |
More?...
 1.5.6