# Tutorials

A number of tutorials are provided to help you getting acquainted with the
Hugin technology and with the Hugin Graphical User Interface. There is one
section of tutorials that introduce some basic concepts, and another that
presents some more advanced features of the Hugin Graphical User Interface.

## Basic
Concepts

- The Paradigms Tutorial presents the
three main paradigms for expert systems: Rule-based systems, Neural
networks, and Bayesian networks.
- The Bayesian Networks Tutorial describes the
basic properties of Bayesian networks, and is recommended if you have no or
little prior knowledge about Bayesian networks.
- The How to Build BNs Tutorial provides a
step-by-step guide to constructing a Bayesian network using the Hugin
Graphical User Interface.
- The Limited Memory Influence Diagrams Tutorial describes the
basic properties of limited memory influence diagrams, and is recommended if you have no or
little prior knowledge about limited memory influence diagrams (LIMIDs).
- The How to Build LIMIDs Tutorial provides a
step-by-step guide to constructing a LIMID using the Hugin
Graphical User Interface.
- The Object Orientation Tutorial describes the
basic properties of object-oriented Bayesian networks and LIMIDs, and is recommended if you have no or little prior knowledge about
this subject.
- The How to Build OOBNs Tutorial provides a
step-by-step guide to constructing an object-oriented Bayesian network using
the Hugin Graphical User Interface.

## Learning More

- The Node Table Tutorial explains the functionalities of node tables.
- The Table Generator Tutorial shows how to specify simple expressions for large tables and then let
the built-in table generator do all the hard work of filling in the
numbers of the table.
- The Case and Data File Formats Tutorial describes
how data for learning may be specified as case and data files.
- The Structure Learning
Tutorial describes how Bayesian networks can be constructed
automatically from data.
- The EM Learning Tutorial describes how
the probabilities (parameters) of Bayesian networks can be learned
automatically from data.
- The Adaptation Tutorial
explains how the probabilities specified for Bayesian networks can be
automatically updated from experience (i.e., evidence) such that, for
example, the networks adapt to changing conditions its environment.
- The Case Generator Tutorial
explains how to generate simulated cases from a Bayesian network.

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