| The Robotics WEBook - Bayesian probability theory |

| | **Bayesian** **probability** theory only began to be re-appreciated in different application domains (first in physics and later in related sciences such as astronomy) during various periods of the 20th century, against a scientific background that was, by then, more or less monopolised by traditional statistics. |

| | The **Bayesian** paradigm is consistent, or, in other words, the **Bayesian** framework is free from paradoxes and internal contradictions: it does not matter in what form or in what order the available information is processed by the **Bayesian** **probability** tools and algorithms, because the result will always be the same. |

| | **Bayesian** theory is no exception to this custom of using graphs to model the structural relationships in a given information processing system: a node contains the information about a set of domain variables, and the edges denote conditional (in)dependence between the variables in the connected nodes. |

| www.roble.info /basicST/stat/bayes-index.php (4318 words) |