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abstract: |-
  The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
  and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art.
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creators_name:
  - family: Bessiere
    given: P
    honourific: Dr
    lineage: ''
date: 2003
date_type: published
datestamp: 2004-08-10
department: Rhone-Alpes
dir: disk0/00/00/37/41
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eprintid: 3741
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institution: 'INRIA - French Institute for Research in Computer Science and Control'
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keywords: 'Bayesian programming, Bayesian modelling, Bayesian reasoning, Bayesian learning, Bayesian networks'
lastmod: 2011-03-11 08:55:39
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note: 'available at: http://www-laplace.imag.fr/publications/Rayons/RR-4730.pdf'
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rev_number: 12
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status_changed: 2007-09-12 16:53:09
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  - comp-sci-robot
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title: Probabilistic Methodology and Techniques for Artefact Conception and Development
type: techreport
userid: 4357
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