Preface

This document will introduce you to the main functions of BayesiaLab by taking you through a very practical guided visit. This is not BayesiaLab Help file (the help system is directly available from the main menu).

Note: This tutorial is in no way a course on bayesian network theory; nevertheless, if this kind of training is required, don't hesitate to contact us at this address: Turn on JavaScript!.

The tutorial is organized as follows:

Chapter 1: Creating a bayesian network

This chapter will show you how to graphically create a bayesian network representing a particular expertise, and from that to define the probabilities and to associate costs to information acquisition.

Chapter 2: Using a bayesian network

In this chapter, the Bayesian network developed in Chapter 1 is taken further using Bayesian inference. The chapter also shows us how to automatically generate an adaptive questionnaire based on a target and how to evaluate the quality of the bayesian network.

Chapter 3: Extracting a bayesian network from a database

Data Mining methods can be used to automatically generate a Bayesian network from the existing data. This chapter will guide you in the use of the various automatic learning methods found in BayesiaLab as well as for the use of tuning tools.

Chapter 4: Analyzing a bayesian network

In this chapter, we will describe the different tools proposed by BayesiaLab to greatly improve the readability and the comprehensibility of the bayesian networks: automatic positioning of the nodes, sensibility analysis tools, and analysis report generation.

Chapter 5: Creating a dynamic bayesian network

The Bayesian networks described in the first few chapters are static bayesian networks and they do not represent a state of knowledge that is dependent on time. This last chapter illustrates the modeling of a dynamic system, one that evolves over time. This modeling uses a class of models called dynamic bayesian networks.

Chapter 6: Learning policies

Bayesian networks are by themselves decision aiding tools. However, this aid can be greatly improved. This chapter will show you how using the Utility nodes for qualifying your states, how defining Decision and lastly how learning automatically policies for your static and dynamic bayesian networks.

Chapter 7: Using Constraint nodes

This last chapter is dedicated to the illustration of the use of the constraint nodes. These special nodes allow specifying constraints that must hold between variables.