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Basic Concepts of Bayesian Programming

  • Pierre Bessière
  • Olivier Lebeltel
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 46)

Abstract

The purpose of this chapter is to introduce gently the basic concepts of Bayesian programming.

After a short formal introduction to Bayesian programming, we present these concepts using three simple experiments with the mobile mini-robot Khepera. These three instances have been selected from the numerous experiments we have conducted with this robot for their simplicity and didactic qualities. A more extensive description of the work done with Khepera may be found in a paper in Advanced Robotics (Lebeltel et al., 2004) or, in even greater detail, in the PhD thesis of Olivier Lebeltel (Lebeltel (1999) in French).

We also present the technical issues related to Bayesian programming: inference principles and algorithms and programming language. Although they are very interesting, we have kept this part very short, as these technical questions are not central to this book.

Keywords

Bayesian Network Joint Distribution Light Sensor Bayesian Belief Network Sensory Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pierre Bessière
    • 1
  • Olivier Lebeltel
    • 2
  1. 1.CNRS - Grenoble Université 
  2. 2.CNRS - LRI Lab 

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