bdm::migamma Class Reference

Inverse-Gamma random walk. More...

#include <exp_family.h>

List of all members.

Public Member Functions

void set_parameters (int len, double k0)
 Set value of k.
void condition (const vec &val)
 Update iepdf so that it represents this mpdf conditioned on rvc = cond This function provides convenient reimplementation in offsprings.
eigammae ()
 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.
void from_setting (const Setting &set)
 Load from structure with elements:.
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.
virtual void validate ()
 This method TODO.
Constructors
 migamma ()
 migamma (const migamma &m)
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

double k
 Constant $k$.
vec & _alpha
 cache of iepdf.alpha
vec & _beta
 cache of iepdf.beta
eigamma iepdf
 Internal epdf used for sampling.
int dimc
 dimension of the condition
RV rvc
 random variable in condition


Detailed Description

Inverse-Gamma random walk.

Mean value, $ \mu $, of this density is given by rvc . Standard deviation of the random walk is proportional to one $ k $-th the mean. This is achieved by setting $ \alpha=\mu/k^2+2 $ and $ \beta=\mu(\alpha-1)$.

The standard deviation of the walk is then: $ \mu/\sqrt(k)$.


Member Function Documentation

void bdm::mpdf::from_setting ( const Setting &  set  )  [virtual, inherited]

Load from structure with elements:.

         { class = "mpdf_offspring",
           rv = {class="RV", names=(...),}; // RV describing meaning of random variable
           rvc= {class="RV", names=(...),}; // RV describing meaning of random variable in condition
           // elements of offsprings
         }

Reimplemented from bdm::root.

Reimplemented in bdm::mepdf, bdm::mprod, bdm::mDirich, bdm::mlnorm< sq_T, TEpdf >, bdm::mgnorm< sq_T >, bdm::mgamma, bdm::migamma_ref, bdm::mlognorm, bdm::mlnorm< chmat >, and bdm::mlnorm< ldmat, enorm >.


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

Generated on Wed Oct 7 17:34:49 2009 for mixpp by  doxygen 1.5.9