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2011

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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Estimating parameters in stochastic systems: A variational Bayesian approach

Vrettas, M. D., Cornford, D. and Opper, M.

Physica D: Nonlinear Phenomena. Elsevier, 1877-1900. 2011

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Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

Macke, J., Opper, M. and Bethge, M.

Physical Review Letters. American Physical Society, 208102. 2011

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2012

Bayesian Inference for Change Points in Dynamical Systems with Reusable States—a Chinese Restaurant Process Approach

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

Proceedings of the International Conference on Artificial Intelligence and Statistics 2012

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Optimal control as a graphical model inference problem

Kappen, H. J., Gomez, V. and Opper, M.

Machine Learning. Springer, 159-182. 2012

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