#include <libEF.h>
Public Member Functions | |
egiw (RV rv, mat V0, double nu0=-1.0) | |
Default constructor, if nu0<0 a minimal nu0 will be computed. | |
egiw (RV rv, ldmat V0, double nu0=-1.0) | |
Full constructor for V in ldmat form. | |
vec | sample () const |
Returns a sample, from density . | |
vec | mean () const |
return expected value | |
vec | variance () const |
return expected variance (not covariance!) | |
void | mean_mat (mat &M, mat &R) const |
double | evallog_nn (const vec &val) const |
In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise val = [theta_1 theta_2 ... r_1 r_2 ]. | |
double | lognc () const |
logarithm of the normalizing constant, | |
ldmat & | _V () |
returns a pointer to the internal statistics. Use with Care! | |
const ldmat & | _V () const |
returns a pointer to the internal statistics. Use with Care! | |
double & | _nu () |
returns a pointer to the internal statistics. Use with Care! | |
const double & | _nu () const |
void | pow (double p) |
Power of the density, used e.g. to flatten the density. | |
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 mat | sample_m (int N) const |
Returns N samples from density . | |
virtual vec | evallog_m (const mat &Val) const |
Compute log-probability of multiple values argument val . | |
virtual mpdf * | condition (const RV &rv) const |
Return conditional density on the given RV, the remaining rvs will be in conditioning. | |
virtual epdf * | marginal (const RV &rv) const |
Return marginal density on the given RV, the remainig rvs are intergrated out. | |
const RV & | _rv () const |
access function, possibly dangerous! | |
void | _renewrv (const RV &in_rv) |
modifier function - useful when copying epdfs | |
Protected Attributes | |
ldmat | V |
Extended information matrix of sufficient statistics. | |
double | nu |
Number of data records (degrees of freedom) of sufficient statistics. | |
int | xdim |
Dimension of the output. | |
int | nPsi |
Dimension of the regressor. | |
RV | rv |
Identified of the random variable. |
For -variate densities, given rv.count() should be V.rows().