# bdm::mlstudent Class Reference

## Detailed Description

(Approximate) Student t density with linear function of mean value

The internal epdf of this class is of the type of a Gaussian (enorm). However, each conditioning is trying to assure the best possible approximation by taking into account the zeta function. See [] for reference.

Perhaps a moment-matching technique?

`#include <exp_family.h>`

List of all members.

## Public Member Functions
void | set_parameters (const mat &A0, const vec &mu0, const ldmat &R0, const ldmat &Lambda0)
| constructor function
void | condition (const vec &cond)
void | validate ()
| This method TODO.
const vec & | _mu_const () const
| access function
const mat & | _A () const
| access function
mat | _R () const
| access function
void | from_setting (const Setting &set)
enorm< ldmat > & | e ()
| access function to iepdf
vec | samplecond (const vec &cond)
| Reimplements samplecond using `condition()`.
double | evallogcond (const vec &val, const vec &cond)
| Reimplements evallogcond using `condition()`.
virtual vec | evallogcond_m (const mat &Dt, const vec &cond)
| Efficient version of evallogcond for matrices.
virtual vec | evallogcond_m (const Array< vec > &Dt, const vec &cond)
| Efficient version of evallogcond for Array<vec>.
virtual mat | samplecond_m (const vec &cond, int N)
| Efficient version of samplecond.
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
void | set_parameters (const mat &A0, const vec &mu0, const ldmat &R0)
| Set `A` and `R`.
Access to attributes
const RV & | _rv () const
const RV & | _rvc () const
int | dimension () const
int | dimensionc ()
Connection to other objects
void | set_rvc (const RV &rvc0)
void | set_rv (const RV &rv0)
bool | isnamed ()
## Protected Member Functions
void | set_ep (epdf &iepdf)
| set internal pointer `ep` to point to given `iepdf`
void | set_ep (epdf *iepdfp)
| set internal pointer `ep` to point to given `iepdf`
## Protected Attributes
ldmat | Lambda
| Variable from theory.
ldmat & | _R
| Reference to variable .
ldmat | Re
| Variable .
mat | A
| Internal epdf that arise by conditioning on `rvc`.
vec | mu_const
| Constant additive term.
enorm< ldmat > | iepdf
| Internal epdf used for sampling.
int | dimc
| dimension of the condition
RV | rvc
| random variable in condition
## Friends
std::ostream & | operator<< (std::ostream &os, mlnorm< sq_M, enorm > &ml)
| Debug stream.

## Member Function Documentation

void bdm::mlstudent::condition | ( | const vec & | cond | ) | `[inline, virtual]`

Set value of `rvc` . Result of this operation is stored in `epdf` use function `_ep` to access it.

Reimplemented from bdm::mlnorm< ldmat, enorm >.

References bdm::mlnorm< ldmat, enorm >::_R(), bdm::mlnorm< ldmat, enorm >::A, bdm::mpdf_internal< enorm< ldmat > >::iepdf, bdm::ldmat::invqform(), Lambda, bdm::mlnorm< ldmat, enorm >::mu_const, Re, and bdm::sqmat::rows().

void bdm::mlnorm< ldmat , enorm >::from_setting | ( | const Setting & | set | ) | `[inline, virtual, inherited]`

Create Normal density with linear function of mean value

from structure

                    class = 'mlnorm<ldmat>', (OR) 'mlnorm<chmat>', (OR) 'mlnorm<fsqmat>';
                    A     = [];                  // matrix or vector of appropriate dimension
                    const = [];                  // vector of constant term
                    R     = [];                  // square matrix of appropriate dimension

Reimplemented from bdm::mpdf.

References bdm::mlnorm< sq_T, TEpdf >::A, bdm::mlnorm< sq_T, TEpdf >::from_setting(), bdm::UI::get(), bdm::mlnorm< sq_T, TEpdf >::mu_const, bdm::mlnorm< sq_T, TEpdf >::set_parameters(), and bdm::mlnorm< sq_T, TEpdf >::validate().

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

- exp_family.h