Publikationen von CE Rasmussen

Konferenzbeitrag (34)

41.
Konferenzbeitrag
Kocijan, J.; Banko, B.; Likar, B.; Girard, A.; Murray-Smith, R.; Rasmussen, C.: A case based comparison of identification with neural network and Gaussian process models. In: Intelligent Control Systems and Signal Processing: ICONS 2003, S. 137 - 142 (Hg. Ruano, E.). IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2003), Faro, Portugal, 08. April 2003 - 11. April 2003. (2003)
42.
Konferenzbeitrag
Quiñonero-Candela, J.; Girard, A.; Larsen, J.; Rasmussen, C.: Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), Bd. 2, S. 701 - 704. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2003), Hong Kong, China, 06. April 2003 - 10. April 2003. IEEE, Piscataway, NJ, USA (2003)
43.
Konferenzbeitrag
Rasmussen, C.: Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals. In: Bayesian Statistics 7, S. 651 - 659 (Hg. Bernardo, J.; Bayarri, J.; Berger, J.; Dawid, A.; Heckerman, D. et al.). Seventh Valencia international meeting, dedicated to Dennis V. Lindley, Arona, Spain, 02. Juni 2002 - 06. Juni 2002. Oxford University Press, Oxford, UK (2003)
44.
Konferenzbeitrag
Rasmussen, C.; Ghahramani , Z.: Occam's Razor. In: Advances in Neural Information Processing Systems 13, S. 294 - 300 (Hg. Leen, T.; Dietterich, T.; Tresp, V.). Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000), Denver, CO, USA, 27. November 2000 - 02. Dezember 2000. MIT Press, Cambridge, MA, USA (2001)
45.
Konferenzbeitrag
Rasmussen, C.: The Infinite Gaussian Mixture Model. In: Advances in Neural Information Processing Systems 12, S. 554 - 560 (Hg. Solla, S.; Leen, T.; Müller, K.). Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), Denver, CO, USA, 29. November 2000 - 04. Dezember 2000. MIT Press, Cambridge, MA, USA (2000)
46.
Konferenzbeitrag
Rasmussen, C.: A practical Monte Carlo implementation of Bayesian learning. In: Advances in Neural Processing Systems 8, S. 598 - 604 (Hg. Touretzky , D.; Mozer, M.; Hasselmo, M.). Ninth Annual Conference on Neural Information Processing Systems (NIPS 1995), Denver, CO, USA, 27. November 1995 - 02. Dezember 1995. MIT Press, Cambridge, MA, USA (1996)
47.
Konferenzbeitrag
Williams, C.; Rasmussen, C.: Gaussian Processes for Regression. In: Advances in Neural Processing Systems 8, S. 514 - 520 (Hg. Touretzky , D.; Mozer, M.; hasselmo, M.). Ninth Annual Conference on Neural Information Processing Systems (NIPS 1995), Denver, CO, USA, 27. November 1995 - 02. Dezember 1995. MIT Press, Cambridge, MA, USA (1996)

Meeting Abstract (1)

48.
Meeting Abstract
Görür, D.; Rasmussen, C.: Dirichlet Process Mixtures of Factor Analysers. In Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5). Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), Valencia, Spain, 14. Juni 2007 - 16. Juni 2007. (2007)

Vortrag (3)

49.
Vortrag
Rasmussen, C.: Advances in Gaussian Processes. Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), Vancouver, BC, Canada (2006)
50.
Vortrag
Görür, D.; Rasmussen, C.: Sampling for non-conjugate infinite latent feature models. 8th Valencia International Meeting on Bayesian Statistics (ISBA 2006), Benidorm, Spain (2006)
51.
Vortrag
Rasmussen, C.; Görür, D.: MCMC inference in (Conditionally) Conjugate Dirichlet Process Gaussian Mixture Models. ICML Workshop on Learning with Nonparametric Bayesian Methods 2006, Pittsburgh, PA, USA (2006)

Poster (2)

52.
Poster
Pfingsten, T.; Kuss, M.; Rasmussen, C.: Nonstationary Gaussian Process Regression using a Latent Extension of the Input Space. Eighth World Meeting of the International Society for Bayesian Analysis (ISBA 2006), Benidorm, Spain (2006)
53.
Poster
Tanner, T.; Hill, N.; Rasmussen, C.; Wichmann, F.: Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain. 8th Tübinger Wahrnehmungskonferenz (TWK 2005), Tübingen, Germany (2005)

Hochschulschrift - Doktorarbeit (1)

54.
Hochschulschrift - Doktorarbeit
Rasmussen, C.: Evaluation of Gaussian Processes and other Methods for Non-Linear Regression. Dissertation, 132 S., University of Toronto, Toronto, Canada (1997)

Bericht (4)

55.
Bericht
Nickisch, H.; Rasmussen, C.: Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 10 S.
56.
Bericht
Kuss, M.; Pfingsten, T.; Csato, L.; Rasmussen, C.: Approximate Inference for Robust Gaussian Process Regression (Technical Report of the Max Planck Institute for Biological Cybernetics, 136). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2005), 27 S.
57.
Bericht
Quiñonero-Candela, J.; Girard, A.; Rasmussen, C.: Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines - Application to Multiple-Step Ahead Time-Series Forecasting. Technical University of Denmark, DTU: Informatics and Mathematical Modelling, Kopenhagen, Denmark (2003), 14 S.
58.
Bericht
Williams, C.; Rasmussen, C.; Scwaighofer, A.; Tresp, V.: Observations on the Nyström Method for Gaussian Process Prediction. University of Edinburgh, Edinburgh, UK (2002), 9 S.
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