bdm::mlognorm Class Reference

Log-Normal random walk. More...

#include <exp_family.h>

List of all members.

Public Member Functions

 mlognorm ()
 Constructor.
void set_parameters (int size, double k)
 Set value of k.
void condition (const vec &val)
 Update ep so that it represents this mpdf conditioned on rvc = cond.
void from_setting (const Setting &set)
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.
Matematical operations
virtual vec samplecond (const vec &cond)
 Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.
virtual mat samplecond_m (const vec &cond, int N)
 Returns.
virtual double evallogcond (const vec &dt, const vec &cond)
 Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently.
virtual vec evallogcond_m (const mat &Dt, const vec &cond)
 Matrix version of evallogcond.
virtual vec evallogcond_m (const Array< vec > &Dt, const vec &cond)
 Array<vec> version of evallogcond.
Access to attributes
RV _rv ()
RV _rvc ()
int dimension ()
int dimensionc ()
epdfe ()
void set_ep (shared_ptr< epdf > ep)
Connection to other objects
void set_rvc (const RV &rvc0)
void set_rv (const RV &rv0)
bool isnamed ()

Protected Attributes

shared_ptr< elognormeno
double sig2
 parameter 1/2*sigma^2
vec & mu
 access
int dimc
 dimension of the condition
RV rvc
 random variable in condition


Detailed Description

Log-Normal random walk.

Mean value, $\mu$, is...

==== Check == vv = Standard deviation of the random walk is proportional to one $k$-th the mean. This is achieved by setting $\alpha=k$ and $\beta=k/\mu$.

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


Member Function Documentation

void bdm::mlognorm::from_setting ( const Setting &  set  )  [virtual]

UI for mlognorm

The mlognorm is constructed from a structure with fields:

                        system = {
                                type = "mlognorm";
                                k = 0.1;                                  // constant k
                                mu0 = [1., 1.];
                                
                                // == OPTIONAL ==
                                // description of y variables
                                y = {type="rv"; names=["y", "u"];};
                                // description of u variable
                                u = {type="rv"; names=[];}
                        };

Reimplemented from bdm::mpdf.

References condition(), bdm::UI::get(), and set_parameters().

vec bdm::mpdf::samplecond ( const vec &  cond  )  [virtual, inherited]

Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.

Parameters:
cond is numeric value of rv

Reimplemented in bdm::mprod.

References bdm::mpdf::condition().

Referenced by bdm::MPF< BM_T >::bayes(), bdm::PF::bayes(), and bdm::ArxDS::step().

mat bdm::mpdf::samplecond_m ( const vec &  cond,
int  N 
) [virtual, inherited]

Returns.

Parameters:
N samples from the density conditioned on cond, $x \sim epdf(rv|cond)$.
cond is numeric value of rv

References bdm::mpdf::condition().


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

Generated on Wed Aug 5 00:07:03 2009 for mixpp by  doxygen 1.5.9