Publikationen von D Görür

Zeitschriftenartikel (1)

Görür, D.; Rasmussen, C.: Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. Journal of Computer Science and Technology 25 (4), S. 653 - 664 (2010)

Konferenzbeitrag (3)

Teh, Y.; Görür, D.; Ghahramani, Z.: Stick-breaking Construction for the Indian Buffet Process. In: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico, S. 556 - 563 (Hg. Meila, M.; Shen, X.). 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), San Juan, Puerto Rico, 21. März 2007 - 24. März 2007. International Machine Learning Society, Madison, WI, USA (2007)
Görür, D.; Jäkel, F.; Rasmussen, C.: A Choice Model with Infinitely Many Latent Features. In: ICML '06: Proceedings of the 23rd International Conference on Machine Learning, S. 361 - 368 (Hg. Cohen, W.; Moore, A.). 23rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, USA, 25. Juni 2006 - 29. Juni 2006. ACM Press, New York, NY, USA (2006)
Görür, D.; Rasmussen, C.; Tolias, A.; Sinz, F.; Logothetis, N.: Modelling Spikes with Mixtures of Factor Analysers. In: Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004, S. 391 - 398 (Hg. Rasmussen, C.; Bülthoff, H.; Schölkopf, B.; Giese, M.). 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004), Tübingen, Germany, 30. August 2004 - 01. September 2004. Springer, Berlin, Germany (2004)

Meeting Abstract (1)

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 (2)

Görür, D.; Rasmussen, C.: Sampling for non-conjugate infinite latent feature models. 8th Valencia International Meeting on Bayesian Statistics (ISBA 2006), Benidorm, Spain (2006)
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)
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