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Balancing a pole

Sobierajski, K. and Sikora, P.

2011

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Synchronization of neural networks by mutual learning and its application to cryptography

Klein, E., Mislovaty, R., Kanter, I., Ruttor, A. and Kinzel, W.

Advances in Neural Information Processing Systems. MIT Press, 689-696. 2005

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Approximate inference for stochastic reaction processes

Ruttor, A., Sanguinetti, G. and Opper, M.

Learning and Inference in Computational Systems Biology. MIT Press, 189-205. 2010

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Approximate parameter inference in a stochastic reaction-diffusion model

Ruttor, A. and Opper, M.

Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010. JMLR, 669-676. 2010

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Approximate inference in continuous time Gaussian-Jump processes

Opper, M., Ruttor, A. and Sanguinetti, G.

Advances in Neural Information Processing Systems 23, 1831–1839. 2010

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Comparing diffusion and weak noise approximations for inference in reaction models

Ruttor, A., Stimberg, F. and Opper, M.

Proceedings of the Fourth International Workshop on Machine Learning in Systems Biology (October 15-16, 2010, Edinburgh, UK), 149–152. 2010

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MCMC for continuous time switching models

Stimberg, F., Ruttor, A. and Opper, M.

NIPS Workshop on Monte Carlo Methods for Modern Applications (December 10, 2010, Whistler, Canada) 2010

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Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling

Stimberg, F., Ruttor, A. and Opper, M.

Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB). Tampere University of Technology, Tampere, Finland, 169–172. 2011

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Inference in continuous-time change-point models

Stimberg, F., Opper, M., Sanguinetti, G. and Ruttor, A.

Advances in Neural Information Processing Systems 24, 2717–2725. 2011

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Analytical Results for the Error in Filtering of Gaussian Processes

Susemihl, A., Opper, M. and Meir, R.

Advances in Neural Information Processing Systems 24. Curran Associates, Inc., 2303–2311. 2011

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Approximate inference for continuous–time Markov processes

Archambeau, C. and Opper, M.

Bayesian Time Series Models. Cambridge University Press, 125–140.. 2011

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