Bayesian Programming and Modelling
Chapter
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.
Preview
Unable to display preview. Download preview PDF.
References
- 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.Horn, K.S.V.: (2012) , http://ksvanhorn.com/bayes/free-bayes-software.html (retrieved in April 16, 2012)
- 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.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.Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT Press (2009)Google Scholar
- 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.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.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.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.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.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.Jensen, F.V., Nielsen, T.D.: Bayesian networks and decision graphs. Springer (2007)Google Scholar
- 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.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.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.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.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.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.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.Jacobs, R.A.: What determines visual cue reliability? TRENDS in Cognitive Sciences 6(8), 345–350 (2002) ReviewMathSciNetCrossRefGoogle Scholar
- 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.Ghahramani, Z., Beal, M.J.: Propagation Algorithms for Variational Bayesian Learning. Neural Information Processing Systems 13 (2001)Google Scholar
- 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.Murphy, K.: The Bayes Net Toolbox for Matlab. Computing Science and Statistics 33 (2001)Google Scholar
- 25.Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nature Reviews Neuroscience 1, 125–132 (2000) ReviewCrossRefGoogle Scholar
- 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.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.Lebeltel, O.: Programmation Bayésienne des Robots. Ph.D. thesis, Institut National Polytechnique de Grenoble, Grenoble, France (1999)Google Scholar
- 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.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.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.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.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.Elfes, A.: Using occupancy grids for mobile robot perception and navigation. IEEE Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
- 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.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