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.

• Paradigms of Expert Systems
  • Rule-Based Systems
  • Neural Networks
  • Bayesian Networks
  • Comparing Neural Networks and Bayesian Networks
• Introduction to Bayesian Network
  • The Apple Tree Example
• How to Build a Bayesian Network
  • Constructing a New Network
  • Compiling the Network
  • Running the Network
  • The Monitor Windows
• Introduction to (Limited Memory) Influence Diagrams
  • The Apple Tree Example
  • Definition of (Limited Memory) Influence Diagrams
  • The Oil Wildcatter Example
  • The Apple Tree Example Continued
• How to Build a (Limited Memory) Influence Diagram
  • Open the Network for Editing
  • Compiling the Limited Memory Influence Diagram
• Introduction to Object-Oriented Networks
  • Interface nodes
  • The Disease Example Revisited
  • Accident Example
• How to Build an Object-Oriented Bayesian Network
  • Creating the Time-Slice Model
  • Creating the Diseases Mode
  • Compiling the Object-Oriented Network
  • Running the Object-Oriented Network
• Node Table Tutorial
  • Modes
  • Types of Tables
  • Displaying a Node Table
• Table Generator Tutorial
  • Sub-Typing of Discrete Nodes
  • Constant Values
  • Model Nodes
  • Simple Examples
  • Operators and Functions
  • Statistical Distributions
  • Syntax for Expressions
• The Case and Data File Formats
  • The Case File Format
  • The Data File Formats
• Case Generator Tutorial
• The Net Language
  • Nodes
  • The Structure of the Model
  • Potentials
  • Global Information
  • Lexical Matters
  • Row-Major Representation
• Structure Learning Tutorial
• EM Learning Tutorial
  • Experience Tables In EM Learning
  • Example EM Learning
• Adaptation Tutorial
  • Experience Tables In Adaptation
  • Example of Adaptation
  • Fading Tables