egiw Class Reference
Gauss-inverse-Wishart density stored in LD form. More...
#include <libEF.h>
Inheritance diagram for egiw:
Collaboration diagram for egiw:
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 | |
| 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. | |
Detailed Description
Gauss-inverse-Wishart density stored in LD form.
For 
-variate densities, given rv.count() should be 
V.rows().
The documentation for this class was generated from the following files:
- work/git/mixpp/bdm/stat/libEF.h
- work/git/mixpp/bdm/stat/libEF.cpp
