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.

Hugin Expert A/S