# OTAgrum documentation¶

## Introduction¶

The aGrUM library provides efficient algorithms to create and manipulate graphical models. A particular case of such models is the class of Bayesian Networks (BN), which is of first interest in association with OpenTURNS.

A *Bayesian network*, *belief network* or *directed acyclic graphical model* is
a probabilistic graphical model that represents a set of random variables and
their conditional dependencies via a directed acyclic graph (DAG).
In this DAG, edges represent conditional dependencies; nodes which are not
connected represent variables which are conditionally independent of each other.
Each node is associated with a probability function that takes as input a
particular set of values for the node’s parent variables and gives the
probability of the variable represented by the node.

The manipulation of a Bayesian network is called *inference*.
Efficient algorithms exist that perform inference and learning of Bayesian networks.

## What is otagrum ?¶

The otagrum module is the link between Bayesian networks built with aGrUM and distributions defined with OpenTURNS.

It offers the ability to:

- define discretized aGrUM distributions from OpenTURNS distributions
- extract marginal distributions of aGrUM Bayesian networks as OpenTURNS distributions