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Query by committee

Seung, H. S., Opper, M. and Sompolinsky, H.

Proceedings of the Fifth Annual Conference on Computational Learning Theory. ACM Press, 287-294. 1992

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Variational Markov Chain Monte Carlo for Inference in Partially Observed Nonlinear Diffusions

Shen, Y., Archambeau, C., Cornford, D. and Opper, M.

Proceedings of the Workshop Inference and Estimation in Probabilistic Time-Series Models, 67-78. 2008

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A comparison of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Shen, Y., Archambeau, C., Cornford, D., Opper, M., Shawe-Taylor, J. and Barillec, R.

Journal of Signal Processing Systems 2009

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Balancing a pole

Sobierajski, K. and Sikora, P.

2011

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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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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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Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data

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

Advances in Neural Information Processing Systems 27, 730-738. 2014

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