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Efficient inference in matrix-variate Gaussian models with iid observation noise

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Stegle,  Oliver
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Lippert,  Christoph
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Borgwardt,  Karsten
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;
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Stegle, O., Lippert, C., Mooij, J., Lawrence, N., & Borgwardt, K. (2012). Efficient inference in matrix-variate Gaussian models with iid observation noise. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 24 (pp. 630-638). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/2011/hash/a732804c8566fc8f498947ea59a841f8-Abstract.html.


Cite as: https://hdl.handle.net/21.11116/0000-000A-ED4E-5
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