---
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.
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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
publication: ~
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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: ~