bdmtoolbox: Use Case #1: Basic Scenarios of BDMToolbox

Use Case #1: Basic Scenarios of BDMToolbox

A typical scenario may look like:

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Where active objects are:

  1. Data Sources (bdm::DS), providing access to various form of recursive data
  2. Bayesian estimators (bdm::BM), providing various exact and approximate algorithms for Bayesian inference
  3. Controllers (bdm::Controller), generating control strategies, both feedback, or feedforward
  4. Loggers (bdm::logger), for storing results of experiments

These objects operate on data vectors, functions and probability densities:

  1. random variable (bdm::RV) is a name of a variable, used as named connectors in the scenarios,
  2. probability density functions, (bdm::pdf), both conditional and unconditional,
  3. functions, (bdm::fnc), of vector arguments,

These classes act as "LEGO-like" bricks which can be composed a wide range of arrangements.

Predefined scenarios implemented in mex are:

Lists of available objects are:

For details of their use, see tutorials:


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