libBM.h File Reference

Bayesian Models (bm) that use Bayes rule to learn from observations. More...

#include "../itpp_ext.h"
#include <map>

Go to the source code of this file.

Classes

class  bdm::bdmroot
 Root class of BDM objects. More...
class  bdm::str
 Structure of RV (used internally), i.e. expanded RVs. More...
class  bdm::RV
 Class representing variables, most often random variables. More...
class  bdm::fnc
 Class representing function $f(x)$ of variable $x$ represented by rv. More...
class  bdm::epdf
 Probability density function with numerical statistics, e.g. posterior density. More...
class  bdm::mpdf
 Conditional probability density, e.g. modeling some dependencies. More...
class  bdm::datalink
 DataLink is a connection between two data vectors Up and Down. More...
class  bdm::datalink_m2e
 data link between More...
class  bdm::datalink_m2m
class  bdm::logger
 Class for storing results (and semi-results) of an experiment. More...
class  bdm::mepdf
 Unconditional mpdf, allows using epdf in the role of mpdf. More...
class  bdm::compositepdf
 Abstract composition of pdfs, will be used for specific classes this abstract class is common to epdf and mpdf. More...
class  bdm::DS
 Abstract class for discrete-time sources of data. More...
class  bdm::BM
 Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities. More...

Typedefs

typedef std::map< string, int > bdm::RVmap

Functions

RV bdm::concat (const RV &rv1, const RV &rv2)
 Concat two random variables.

Variables

ivec bdm::RV_SIZES
Array< string > bdm::RV_NAMES


Detailed Description

Bayesian Models (bm) that use Bayes rule to learn from observations.

Author:
Vaclav Smidl.
----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty

Using IT++ for numerical operations -----------------------------------


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