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Volumn 31, Issue 4, 2011, Pages 383-400

A Bayesian nonparametric approach to modeling motion patterns

Author keywords

Bayesian nonparametric modeling; Interception and tracking; Markov decision process

Indexed keywords

ADAPTIVE REPRESENTATIONS; COMPLETE INFORMATION; COMPLEX MOTION; DIRICHLET PROCESS; FITTING PARAMETERS; MARKOV DECISION PROCESSES; MIXTURE OF GAUSSIANS; MODELING MOTIONS; MOTION MODELS; MOTION PATTERN; NON-PARAMETRIC MODEL; NON-PARAMETRIC MODELING; NONPARAMETRIC APPROACHES; PARAMETRIC MODELS; TARGET MOTIONS; TRAINING DATA;

EID: 84655169260     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10514-011-9248-x     Document Type: Article
Times cited : (133)

References (33)
  • 1
    • 84875644869 scopus 로고    scopus 로고
    • Using GPS to learn significant locations and predict movement across multiple users
    • Ashbrook, D., and Starner, T. (2003). Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), 275-286.
    • (2003) Personal and Ubiquitous Computing , vol.7 , Issue.5 , pp. 275-286
    • Ashbrook, D.1    Starner, T.2
  • 4
    • 84898947911 scopus 로고    scopus 로고
    • Sparse representation for Gaussian process models
    • Cambridge: MIT Press
    • Csató, L., and Opper, M. (2001). Sparse representation for Gaussian process models. In Advances in neural information processing systems (pp. 444-450). Cambridge: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , pp. 444-450
    • Csat́, L.1    Opper, M.2
  • 6
    • 0036815671 scopus 로고    scopus 로고
    • An agent-based approach to modelling driver route choice behaviour under the influence of real-time information
    • DOI 10.1016/S0968-090X(02)00025-6, PII S0968090X02000256
    • Dia, H. (2002). An agent-based approach to modeling driver route choice behaviour under the influence of real-time information. The American Statistician, 10, 331-349. (Pubitemid 35420129)
    • (2002) Transportation Research Part C: Emerging Technologies , vol.10 , Issue.5-6 , pp. 331-349
    • Dia, H.1
  • 9
    • 84867040604 scopus 로고    scopus 로고
    • Gaussian process priors with uncertain inputs-application to multiple-step ahead time series forecasting
    • Cambridge: MIT Press
    • Girard, A., Rasmussen, C. E., Quintero-Candela, J., and Murraysmith, R. (2003). Gaussian process priors with uncertain inputs-application to multiple-step ahead time series forecasting. In Advances in neural information processing systems (pp. 529-536). Cambridge: MIT Press.
    • (2003) Advances in Neural Information Processing Systems , pp. 529-536
    • Girard, A.1    Rasmussen, C.E.2    Quintero-Candela, J.3    Murraysmith, R.4
  • 12
    • 84878470306 scopus 로고    scopus 로고
    • A Bayesian nonparametric approach to modeling mobility patterns
    • Joseph, J. M., Doshi-Velez, F., and Roy, N. (2010). A Bayesian nonparametric approach to modeling mobility patterns. In AAAI.
    • (2010) AAAI
    • Joseph, J.M.1    Doshi-Velez, F.2    Roy, N.3
  • 13
    • 67650998865 scopus 로고    scopus 로고
    • GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
    • Ko, J., and Fox, D. (2009). GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models. Autonomous Robots, 27(1), 75-90.
    • (2009) Autonomous Robots , vol.27 , Issue.1 , pp. 75-90
    • Ko, J.1    Fox, D.2
  • 15
    • 38049049588 scopus 로고    scopus 로고
    • Trip router with individualized preferences (TRIP): Incorporating personalization into route planning
    • Letchner, J., Krumm, J., and Horvitz, E. (2006). Trip router with individualized preferences (TRIP): Incorporating personalization into route planning. In AAAI.
    • (2006) AAAI
    • Letchner, J.1    Krumm, J.2    Horvitz, E.3
  • 16
    • 34147182909 scopus 로고    scopus 로고
    • Learning and inferring transportation routines
    • DOI 10.1016/j.artint.2007.01.006, PII S0004370207000380
    • Liao, L., Patterson, D. J., Fox, D., and Kautz, H. (2007). Learning and inferring transportation routines. Artificial Intelligence, 171(5-6), 311-331. (Pubitemid 46560477)
    • (2007) Artificial Intelligence , vol.171 , Issue.5-6 , pp. 311-331
    • Liao, L.1    Patterson, D.J.2    Fox, D.3    Kautz, H.4
  • 17
    • 51649083000 scopus 로고    scopus 로고
    • An alternative infinite mixture of Gaussian process experts
    • Meeds, E., and Osindero, S. (2006). An alternative infinite mixture of Gaussian process experts. In NIPS 18.
    • (2006) NIPS , pp. 18
    • Meeds, E.1    Osindero, S.2
  • 18
    • 0001115997 scopus 로고    scopus 로고
    • Developments in the modelling of nonstationary spatial covariance structure from space-time monitoring data
    • E. Baafi & N. Schofield (Eds.), Dordrecht: Kluwer
    • Meiring, W., Monestiez, P., Sampson, P., and Guttorp, P. (1997). Developments in the modelling of nonstationary spatial covariance structure from space-time monitoring data. In E. Baafi & N. Schofield (Eds.), Geostatistics wallongong 1996 (pp. 162-173). Dordrecht: Kluwer.
    • (1997) Geostatistics Wallongong 1996 , pp. 162-173
    • Meiring, W.1    Monestiez, P.2    Sampson, P.3    Guttorp, P.4
  • 19
    • 63749125729 scopus 로고    scopus 로고
    • A POMDP framework for coordinated guidance of autonomous UAVs for multitarget tracking
    • doi:10.1155/2009/724597
    • Miller, S. A., Harris, Z. A., and Chong, E. K. P. (2009). A POMDP framework for coordinated guidance of autonomous UAVs for multitarget tracking. EURASIP Journal on Advances in Signal Processing. doi:10.1155/2009/724597.
    • (2009) EURASIP Journal on Advances in Signal Processing.
    • Miller, S.A.1    Harris, Z.A.2    Chong, E.K.P.3
  • 21
  • 24
    • 84936421967 scopus 로고
    • Choosing models for cross-classifications
    • Raftery, A. (1986). Choosing models for cross-classifications. American Sociological Review, 51, 145-146.
    • (1986) American Sociological Review , vol.51 , pp. 145-146
    • Raftery, A.1
  • 25
    • 79955803023 scopus 로고    scopus 로고
    • The infinite Gaussian mixture model
    • Rasmussen, C. E. (2000). The infinite Gaussian mixture model. In NIPS (p. 12).
    • (2000) NIPS , pp. 12
    • Rasmussen, C.E.1
  • 26
    • 84896062664 scopus 로고    scopus 로고
    • Infinite mixtures of Gaussian process experts
    • Rasmussen, C. E., and Ghahramani, Z. (2002). Infinite mixtures of Gaussian process experts. In NIPS (p. 14).
    • (2002) NIPS , pp. 14
    • Rasmussen, C.E.1    Ghahramani, Z.2
  • 30
    • 84864038646 scopus 로고    scopus 로고
    • Sparse Gaussian processes using pseudo-inputs
    • Cambridge: MIT Press
    • Snelson, E., and Ghahramani, Z. (2006). Sparse Gaussian processes using pseudo-inputs. In Advances in neural information processing systems (Vol. 18, pp. 1257-1264). Cambridge: MIT Press.
    • (2006) Advances in Neural Information Processing Systems , vol.18 , pp. 1257-1264
    • Snelson, E.1    Ghahramani, Z.2
  • 33
    • 85148975703 scopus 로고    scopus 로고
    • Maximum entropy inverse reinforcement learning
    • Ziebart, B. D., Maas, A., Bagnell, J., and Dey, A. K. (2008). Maximum entropy inverse reinforcement learning. In AAAI.
    • (2008) AAAI
    • Ziebart, B.D.1    Maas, A.2    Bagnell, J.3    Dey, A.K.4


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