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C

Bayesian Analysis of the Scatterometer Wind Retrieval inverse Problem: Some new Approaches

Cornford, D., Csató, L., Evans, D. J. and Opper, M.

Journal Royal Statistical Society B, 1–17. 2004

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Data Assimilation with Sequential Gaussian Processes

Csató, L., Cornford, D. and Opper, M.

Uncertainty in geometrical computation, published by: Kluwer. Kluwer, 29-40. 2002

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TAP Gibbs Free Energy, Belief Propagation and Sparsity

Csató, L., Opper, M. and Winther, O.

Advances in Neural Information Processing Systems 14. MIT Press, 657-663. 2002

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Online Learning of Wind-Field Models

Csató, L., Cornford, D. and Opper, M.

Proceedings of the International Conference on Artificial Neural Networks 2001. Springer–Verlag, 300-307. 2001

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Sparse Representation for Gaussian Process Models

Csató, L. and Opper, M.

Advances in Neural Information Processing Systems 13. MIT Press, 444-450. 2001

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Efficient Approaches to Gaussian Process Classification

Csató, L., Fokou´e, E., Opper, M., Schottky, B. and Winther, O.

Advances in Neural Information Processing Systems 12. MIT Press, 251-257. 2000

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Tractable Inference for Probabilistic Data Models

Csató, L., Opper, M. and Winther, O.

Complexity, 64-68. 2003

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Sparse On-Line Gaussian Processes

Csató, L. and Opper, M.

Neural Computation, 641 - 668. 2002

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Expectation propagation for continuous time stochastic processes

Cseke, B., Schnoerr, D., Opper, M. and Sanguinetti, G.

Journal of Physics A: Mathematical and Theoretical. IOPscience, 494002. 2016

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Approximate inference in latent Gaussian-Markov models from continuous time observations

Cseke, B., Opper, M. and Sanguinetti, G.

Advances in Neural Information Processing Systems, 971–979. 2013

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D

Replicators with Random Interactions- a Solvable Model

Diederich, S. and Opper, M.

Phys. Rev. A (Rapid Comm.), 4333. 1989

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Learning by Error Correction in Spin Glass Models of Neural Networks

Diederich, S., Opper, M., Henkel, R. D. and Kinzel, W.

Computer Simulations in Brain Science. Cambridge University Press. 1988

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