Common Bayesian Models for Common Cognitive Issues
Tóm tắt
Từ khóa
Tài liệu tham khảo
Alais D, Burr D (2004) The ventriloquist effect results from near-optimal bimodal integration. Curr Biol 14:257–262
Anastasio TJ, Patton PE, Belkacem-Boussaid K (2000) Using Bayes’ rule to model multisensory enhancement in the superior colliculus. Neural Comput 12(5):1165–1187
Arulampalam S, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filter for online nonlinear/non-gaussian Bayesian tracking. IEEE Transact Signal Proc 50(2):174–188
Battaglia PW, Jacobs RA, Aslin RN (2003) Bayesian integration of visual and auditory signals for spatial localization. J Opt Soc Am A 20(7):1391–1397
Bengio Y, Frasconi P (1995) An input/output HMM architecture. In: Tesauro G, Touretzky D, Leen T (eds) Advances in neural information processing systems 7. MIT Press, Cambridge, pp 427–434
Bessière P, Ahuactzin J-M, Aycard O, Bellot D, Colas F, Coué C, Diard J, Garcia R, Koike C, Lebeltel O, LeHy R, Malrait O, Mazer E, Mekhnacha K, Pradalier C, Spalanzani A (2003) Survey: Probabilistic methodology and techniques for artefact conception and development. Technical Report RR-4730, INRIA Rhône-Alpes, Montbonnot, France
Bessière P, Laugier C, Siegwart R (eds) (2008) Probabilistic reasoning and decision making in sensory-motor systems, vol 46 of STAR. Springer, Berlin
Bishop CM, Svensén M (2003) Bayesian hierarchical mixtures of experts. In: Proceedings of the ninteenth conference on uncertainty in artificial intelligence. Acapulco, Mexico
Boutilier C, Dean T, Hanks S (1999) Decision theoretic planning: Structural assumptions and computational leverage. J Artif Intell Res (JAIR) 11:1–94
Brockwell PJ, Davis RA (2000) Introduction to time series and forecasting, 2nd edn. Springer, Berlin
Dean T, Kanazawa K (1989) A model for reasoning about persistence and causation. Comput Intell 5(3):142–150
Diard J, Bessière P (2008) Bayesian maps: probabilistic and hierarchical models for mobile robot navigation. In: Bessière P, Laugier C, Siegwart R (eds) Probabilistic reasoning and decision making in sensory-motor systems, vol 46. Springer Tracts in Advanced Robotics. Springer, Berlin, pp 153–176
Drewing K, Ernst M (2006) Integration of force and position cues for shape perception through active touch. Brain Res 1078:92–100
Ernst MO, Banks MS (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415(6870):429–433
Frey BJ (1998) Graphical models for machine learning and digital communication. MIT Press, Cambridge
Geisler WS, Kersten D (2002) Illusions, perception and Bayes. Nature Neuroscience 5(6):598–604
Gepshtein S, Banks MS (2003) Viewing geometry determines how vision and haptics combine in size perception. Curr Biol 13(6):483–488
Ghahramani Z, Wolpert DM, Jordan MI (1997) Computational models of sensorimotor integration. In: Morasso PG, Sanguineti V (eds) Self-organization, computational maps and motor control. Elsevier, Amsterdam, pp 117–147
Gopnik A, Schulz L (2004) Mechanisms of theory formation in young children. Trends Cogn Sci 8(8):371–377
Haith A, Jackson C, Miall C, Vijayakumar S (2008) Unifying the sensory and motor components of sensorimotor adaptation. In: Advances in neural information processing systems (NIPS 2008)
Harvey AC (1992) Forecasting, structural time series models and the Kalman filter. Cambridge University Press, Cambridge.
Hauskrecht M, Meuleau N, Boutilier L, Kaelbling L, Dean T (1998) Hierarchical solution of markov decision processes using macro-actions. In: Proceedings of the 14-th conference on uncertainty in artificial intelligence. pp 220–229.
Hillis JM, Watt SJ, Landy MS, Banks MS (2004) Slant from texture and disparity cues: optimal cue combination. J Vis 4:967–992
Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3:79–87
Jensen F (1996) An introduction to Bayesian networks. UCL Press, London
Jürgens R, Becker W (2006) Perception of angular displacement without landmarks: evidence for Bayesian fusion of vestibular, optokinetic, podokinesthetic, and cognitive information. Exp Brain Res 174:528–543
Kaelbling LP, Littman M, Cassandra A (1998) Planning and acting in partially observable stochastic domain. Artifi Intell 101(1–2):99–134
Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME–J Basic Eng 82(Series D):35–45
Kemp C, Tenenbaum J (2008) The discovery of structural form. Proc Natl Acad Sci USA 105(31):10687–10692
Kersten D, Mamassian P, Yuille A (2004) Object perception as Bayesian inference. Ann Rev Psychol 55:271–304
Kiemel T, Oie K, Jeka J (2002) Multisensory fusion and the stochastic structure of postural sway. Biol Cybern 87:262–277
Koike C (2005) Bayesian approach to action selection and attention focusing. Application in autonomous robot programming. Thèse de doctorat, Inst. Nat. Polytechnique de Grenoble, Grenoble (FR)
Koike C, Bessière P, Mazer E (2008) Bayesian approach to action selection and attention focusing. In: Bessière P, Laugier C, Siegwart R (eds) Probabilistic reasoning and decision making in sensory-motor systems, vol 46 of STAR. Springer, Berlin
Koller D, Pfeffer A (1997) Object-oriented Bayesian networks. In: Proceedings of the thirteenth conference on uncertainty in artifical intelligence. Morgan Kaufmann publishers, San Francisco, pp 302–313
Körding KP, Beierholm U, Ma WJ, Quartz S, Tenenbaum JB, Shams L (2007) Causal inference in multisensory perception. PLoS one 2(9):e943
Körding KP, Wolpert DM (2004b) The loss function of sensorimotor learning. Proc Natl Acad Sci USA 101(26):9839–9842
Landy MS, Maloney LT, Johnston EB, Young M (1995) Measurement and modeling of depth cue combination: in defense of weak fusion. Vis Res 35:389–412
Laskey KB, Mahoney SM (1997) Network fragments: representing knowledge for constructing probabilistic models. In: Proceedings of the thirteenth conference on uncertainty in artifical intelligence. Morgan Kaufmann publishers, San Francisco, pp 334–341
Laurens J, Droulez J (2008) Bayesian modeling of visuo-vestibular interactions. In: Bessière P, Laugier C, Siegwart R (eds) Probabilistic reasoning and decision making in sensory-motor systems, vol 46 of STAR. Springer, Berlin
Lebeltel O, Bessière P, Diard J, Mazer E (2004) Bayesian robot programming. Adv Robot 16(1):49–79
Leonard J, Durrant-Whyte H, Cox I (1992) Dynamic map-building for an autonomous mobile robot. Intl J Robot Res 11(4):286–298
Maeda S (1990) Compensatory articulation during speech: evidence from the analysis and synthesis of vocal-tract shapes using an articulatory model. In: Hardcastle WJ, Marchal A (eds) Speech production and speech modelling. Kluwer, Dordrecht, pp 131–149
Mitchell TM (1997) Machine Learning. McGraw-Hill, Hall
Murphy K (2002) Dynamic Bayesian networks: Representation, Inference and Learning. Ph.D. thesis, University of California, Berkeley, Berkeley, CA
Neal RM, Beal MJ, and Roweis ST (2003) Inferring state sequences for non-linear systems with embedded hidden Markov models. In: Thrun S, and al, (eds), Advances in neural information processing systems 16. MIT Press, Cambridge
Nefian A, Hayes M (1999) Face recognition using an embedded hmm. In: Proceedings of the IEEE conference on audio and video-based biometric person authentication. pp 19–24
Pearl J (1988) Probabilistic reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo
Pineau J, Thrun S (2002) High-level robot behaviour control with POMDPs. In: AAAI workshop on cognitive robotics
Poggio T (1984) Vision by man and machine. Sci Am 250:106–116
Pradalier C, Colas F, Bessière P (2003) Expressing Bayesian fusion as a product of distributions: applications in robotics. In: Proceedings IEEE international conference on intelligent robots and systems
Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
Rabiner LR, Juang B-H (1993) Fundamentals of speech recognition, chapter theory and implementation of Hidden Markov Models. Prentice Hall, Englewood Cliffs, pp 321–389
Robinson JA (1979) Logic: form and function. North-Holland, New York
Sato Y, Toyoizumi T, Aihara K (2007) Bayesian inference explains perception of unity and ventriloquism aftereffect: Identification of common sources of audiovisual stimuli. Neural Comput 19(12):3335–3355
Stocker A, Simoncelli E (2008) A Bayesian model of conditioned perception. In: Platt J, Koller D, Singer Y, Roweis S (eds) Advances in neural information processing systems 20. MIT Press, Cambridge, pp 1409–1416
Thrun S (2000) Probabilistic algorithms in robotics. AI Magazine 21(4):93–109
Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge
van der Kooij H, Jacobs R, Koopman B, Grootenboer H (1999) A multisensory integration model of human stance control. Biol Cybern 80:299–308
Waterhouse S, MacKay D, Robinson T (1996) Bayesian methods for mixtures of experts. In: Touretzky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems, vol 8. The MIT Press, Cambridge, pp 351–357
Weiss Y, Simoncelli EP, Adelson EH (2002) Motion illusions as optimal percepts. Nature Neurosci 5(6):598–604
Yuille AL, Bülthoff HH (1996) Bayesian decision theory and psychophysics. In: Knill DC, Richards W (eds) Perception as Bayesian inference. MIT Press, Cambridge, pp 123–161