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Bayesian Programming and Modelling

  • João Filipe Ferreira
  • Jorge Dias
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 91)

Abstract

A vast amount of different formalisms exist for the construction of probabilistic models (Fig. 3.1):

  • General formalisms, which allow the construction of more encompassing and potentially more complete models.

  • Specific formalisms, which yield simpler or more intuitive formulations, thus allowing for easier or more efficient computation.

Keywords

Bayesian Network Sensor Model Occupancy Grid Exact Inference Occupied Cell 
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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References

  1. 1.
    Ferreira, J.F., Castelo-Branco, M., Dias, J.: A hierarchical Bayesian framework for multimodal active perception. Adaptive Behavior 20(3), 172–190 (2012), doi:10.1177/1059712311434662CrossRefGoogle Scholar
  2. 2.
    Horn, K.S.V.: (2012) , http://ksvanhorn.com/bayes/free-bayes-software.html (retrieved in April 16, 2012)
  3. 3.
    Colas, F., Diard, J., Bessiére, P.: Common Bayesian Models For Common Cognitive Issues. Acta Biotheoretica 58(2-3), 191–216 (2010)CrossRefGoogle Scholar
  4. 4.
    Faria, D.R., Martins, R., Lobo, J., Dias, J.: Probabilistic Representation of 3D Object Shape by In-Hand Exploration. In: Proceedings of The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2010, Taipei, Taiwan (2010)Google Scholar
  5. 5.
    Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT Press (2009)Google Scholar
  6. 6.
    Lunn, D., Spiegelhalter, D., Thomas, A., Best, N.: The BUGS project: Evolution, critique and future directions. Statistics in Medicine 28, 3049–3082 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bessiére, P., Laugier, C., Siegwart, R. (eds.): Probabilistic Reasoning and Decision Making in Sensory-Motor Systems. STAR, vol. 46. Springer, Heidelberg (2008) ISBN 978-3-540-79006-8MATHGoogle Scholar
  8. 8.
    Ferreira, J.F., Bessiére, P., Mekhnacha, K., Lobo, J., Dias, J., Laugier, C.: Bayesian Models for Multimodal Perception of 3D Structure and Motion. In: International Conference on Cognitive Systems (CogSys 2008), pp. 103–108. University of Karlsruhe, Karlsruhe (2008a)Google Scholar
  9. 9.
    Ferreira, J.F., Pinho, C., Dias, J.: Bayesian Sensor Model for Egocentric Stereovision. In: 14a Conferência Portuguesa de Reconhecimento de Padrões Coimbra, RECPAD 2008 (2008)Google Scholar
  10. 10.
    Tay, C., Mekhnacha, K., Chen, C., Yguel, M., Laugier, C.: An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments. International Journal of Autonomous Vehicles 6(1-2), 155–171 (2008)CrossRefGoogle Scholar
  11. 11.
    Yguel, M., Aycard, O., Laugier, C.: Efficient GPU-based Construction of Occupancy Grids Using several Laser Range-finders. International Journal of Autonomous Vehicles 6(1-2), 48–83 (2008)CrossRefGoogle Scholar
  12. 12.
    Jensen, F.V., Nielsen, T.D.: Bayesian networks and decision graphs. Springer (2007)Google Scholar
  13. 13.
    Mekhnacha, K., Ahuactzin, J.M., Bessiére, P., Mazer, E., Smail, L.: Exact and approximate inference in ProBT. Revue d’Intelligence Artificielle 21(3), 295–332 (2007)CrossRefGoogle Scholar
  14. 14.
    Coué, C., Pradalier, C., Laugier, C., Fraichard, T., Bessiére, P.: Bayesian occupancy filtering for multitarget tracking: an automotive application. Int. Journal of Robotics Research 25(1), 19–30 (2006)CrossRefGoogle Scholar
  15. 15.
    Born, R.T., Bradley, D.C.: Structure and Function of Visual Area MT. Annual Review of Neuroscience 28, 157–189 (2005), doi:10.1146/annurev.neuro.26.041002.131052CrossRefGoogle Scholar
  16. 16.
    Rao, R.P.N.: Bayesian inference and attentional modulation in the visual cortex. NeuroReport — Cognitive Neuroscience and Neurophysiology 16(16), 1843–1848 (2005) ISSN 0899-7667Google Scholar
  17. 17.
    Knill, D.C., Pouget, A.: The Bayesian brain: the role of uncertainty in neural coding and computation. TRENDS in Neurosciences 27(12), 712–719 (2004)CrossRefGoogle Scholar
  18. 18.
    Barber, M.J., Clark, J.W., Anderson, C.H.: Neural representation of probabilistic information. Neural Computation 15(8), 1843–1864 (2003), ISSN 0899-7667, doi:10.1162/08997660360675062 MATHCrossRefGoogle Scholar
  19. 19.
    Diard, J., Bessiere, P., Mazer, E.: A survey of probabilistic models using the Bayesian programming methodology as a unifying framework. In: International Conference on Computational Intelligence, Robotics and Autonomous Systems (IEEE-CIRAS), Singapore (2003)Google Scholar
  20. 20.
    Jacobs, R.A.: What determines visual cue reliability? TRENDS in Cognitive Sciences 6(8), 345–350 (2002) ReviewMathSciNetCrossRefGoogle Scholar
  21. 21.
    Valtorta, M., Kim, Y.G., Vomlel, J.: Soft evidential update for probabilistic multiagent systems. International Journal of Approximate Reasoning 29(71), 106 (2002)MathSciNetGoogle Scholar
  22. 22.
    Ghahramani, Z., Beal, M.J.: Propagation Algorithms for Variational Bayesian Learning. Neural Information Processing Systems 13 (2001)Google Scholar
  23. 23.
    Minka, T.P.: Expectation Propagation for approximate Bayesian inference. In: UAI 2001, Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  24. 24.
    Murphy, K.: The Bayes Net Toolbox for Matlab. Computing Science and Statistics 33 (2001)Google Scholar
  25. 25.
    Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nature Reviews Neuroscience 1, 125–132 (2000) ReviewCrossRefGoogle Scholar
  26. 26.
    Treue, S., Hol, K., Rauber, H.J.: Seeing multiple directions of motion — physiology and psychophysics. Nature Neuroscience 3(3), 270–276 (2000)CrossRefGoogle Scholar
  27. 27.
    Denéve, S., Latham, P.E., Pouget, A.: Reading population codes: a neural implementation of ideal observers. Nature Neuroscience 2(8), 740–745 (1999), doi:10.1038/11205CrossRefGoogle Scholar
  28. 28.
    Lebeltel, O.: Programmation Bayésienne des Robots. Ph.D. thesis, Institut National Polytechnique de Grenoble, Grenoble, France (1999)Google Scholar
  29. 29.
    Julier, S.J., Uhlmann, J.K.: A New Extension of the Kalman Filter to Nonlinear Systems. In: Kadar, I. (ed.) Signal Processing, Sensor Fusion, and Target Recognition VI. SPIE Proceedings, vol. 3068, pp. 182–193 (1997)Google Scholar
  30. 30.
    Zemel, R.S., Dayan, P., Pouget, A.: Probabilistic Interpretation of Population Codes. Advances in Neural Information Processing Systems 9, 676–683 (1997)Google Scholar
  31. 31.
    Buntine, W.L.: Operations for Learning with Graphical Models. Journal of Artificial Intelligence Research (AI Access Foundation) 2, 159–225 (1994) ISSN 11076-9757Google Scholar
  32. 32.
    Elfes, A.: Multi-Source Spatial Data Fusion Using Bayesian Reasoning. In: Abidi, M.A., Gonzalez, R.C. (eds.) Data Fusion in Robotics and Machine Intelligence. Academic Press (1992)Google Scholar
  33. 33.
    Charniak, E.: Bayesian networks without tears: making Bayesian networks more accessible to the probabilistically unsophisticated. AI Magazine 12(4), 50–63 (1991)Google Scholar
  34. 34.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. IEEE Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  35. 35.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, revised second printing edn. Morgan Kaufmann Publishers, Inc., Elsevier (1988)Google Scholar
  36. 36.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME - Journal of Basic Engineering 82, 35–45 (1960)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Instituto de Sistemas e Robotica, Departamento de Engenharia Electrotécnica e Computadores Pinhal de Marrocos, Pólo II Universidade de CoimbraCoimbraPortugal

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