---
abstract: |-
  This paper presents a survey of the most common
  probabilistic models for artefact conception. We use
  a generic formalism called Bayesian Programming,
  which we introduce briefly, for reviewing the main
  probabilistic models found in the literature. Indeed,
  we show that Bayesian Networks, Markov Localization,
  Kalman filters, etc., can all be captured under this single
  formalism. We believe it oers the novice reader a
  good introduction to these models, while still providing
  the experienced reader an enriching global view of the
  field.
altloc: []
chapter: ~
commentary: ~
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confdates: 2003
conference: 'International Conference on Computational Intelligence, Robotics and Autonomous Systems (IEEE-CIRAS)'
confloc: Singapore
contact_email: ~
creators_id: []
creators_name:
  - family: Diard
    given: J
    honourific: ''
    lineage: ''
  - family: Bessiere
    given: P
    honourific: ''
    lineage: ''
  - family: Mazer
    given: E
    honourific: ''
    lineage: ''
date: 2003
date_type: published
datestamp: 2004-08-10
department: ~
dir: disk0/00/00/37/55
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editors_id: []
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eprint_status: archive
eprintid: 3755
fileinfo: /style/images/fileicons/application_pdf.png;/3755/1/Diard03a.pdf
full_text_status: public
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ispublished: pub
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keywords: ~
lastmod: 2011-03-11 08:55:39
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metadata_visibility: show
note: ~
number: ~
pagerange: ~
pubdom: TRUE
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refereed: FALSE
referencetext: ~
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:53:17
subjects:
  - comp-sci-robot
succeeds: ~
suggestions: ~
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thesistype: ~
title: A Survey of Probabilistic Models Using the Bayesian Programming Methodology as a Unifying Framework
type: confpaper
userid: 4357
volume: ~
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