.. _Tutorials:

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Tutorials
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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 :ref:`Paradigms Tutorial<Paradigms_Of_Expert_Systems>` presents the three main paradigms for expert systems: Rule-based systems, Neural networks, and Bayesian networks.
* The :ref:`Bayesian Networks Tutorial<Introduction_To_Bayesian_Networks>` describes the basic properties of Bayesian networks, and is recommended if you have no or little prior knowledge about Bayesian networks.
* The :ref:`How to Build BNs Tutorial<HowToBuildABayesianNetwork>` provides a step-by-step guide to constructing a Bayesian network using the HUGIN Graphical User Interface.
* The :ref:`Limited Memory Influence Diagrams Tutorial<Introduction_To_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 :ref:`How to Build LIMIDs Tutorial<How_To_Build_A_Limited_Memory_Influence_Diagram>` provides a step-by-step guide to constructing a LIMID using the HUGIN Graphical User Interface.
* The :ref:`Object Orientation Tutorial<Introduction_To_Object_Oriented_Networks>` 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 :ref:`How to Build OOBNs Tutorial<How_To_Build_An_Object_Oriented_Bayesian_Network>` provides a step-by-step guide to constructing an object-oriented Bayesian network using the HUGIN Graphical User Interface.


Learning More
-------------

* The :ref:`Node Table Tutorial<node_table_tutorial>` explains the functionalities of node tables.
* The :ref:`Table Generator Tutorial<Table_Generator>` 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 :ref:`Case and Data File Formats Tutorial<Case_And_Data_File_Formats>` describes how data for learning may be specified as case and data files.
* The :ref:`Structure Learning Tutorial<Structure_Learning_tutorial>` describes how Bayesian networks can be constructed automatically from data.
* The :ref:`EM Learning Tutorial<EM_Learning_tutorial>` describes how the probabilities (parameters) of Bayesian networks can be learned automatically from data.
* The :ref:`Adaptation Tutorial<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 :ref:`Case Generator Tutorial <Case_Generator_Tutorial>` explains how to generate simulated cases from a Bayesian network.


.. toctree::
   :maxdepth: 2

   ParadigmsOfExpertSystems/ParadigmsOfExpertSystems

   BayesianNetwork/IntroductionToBayesianNetworks
   BayesianNetwork/HowToBuildABayesianNetwork

   LIMID/IntroductionToLimitedMemoryInfluenceDiagrams
   LIMID/HowToBuildALimitedMemoryInfluenceDiagram

   ObjectOrientedBayesianNetwork/IntroductionToObjectOrientedNetworks
   ObjectOrientedBayesianNetwork/HowToBuildAnObjectOrientedBayesianNetwork

   Tables/NodeTable
   Tables/TableGenerator

   CaseAndData/CaseAndDataFileFormats
   CaseAndData/CaseGeneratorTutorial
   CaseAndData/NetLanguage

   Structure_EM_Adaptation_Learning/StructureLearningTutorial
   Structure_EM_Adaptation_Learning/EM_LearningTutorial
   Structure_EM_Adaptation_Learning/AdaptationTutorial



