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Publikationsliste

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2011

Analytical Results for the Error in Filtering of Gaussian Processes [21]

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

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

Link zur Publikation [22] Download Bibtex Eintrag [23]

Approximate inference for continuous–time Markov processes [24]

Archambeau, C. and Opper, M.

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

Link zur Publikation [25] Download Bibtex Eintrag [26]

Estimating parameters in stochastic systems: A variational Bayesian approach [27]

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

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

Download Bibtex Eintrag [28]

Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity [29]

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

Physical Review Letters. American Physical Society, 208102. 2011

Download Bibtex Eintrag [30]

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models [31]

Ribeiro, F. and Opper, M.

Neural Computation. MIT Press, 1047–1069. 2011

Download Bibtex Eintrag [32]

Bayesian Segmentation of Natural Scenes using Dependent Pitman Yor Processes [33]

Thiel, S.

2011 TU Berlin

Link zur Publikation [34] Download Bibtex Eintrag [35]

2010

Comparing Markov Chain Monte Carlo Proposal Densities for Diffusion Processes [36]

Stimberg, F.

2010 TU Berlin

Link zur Publikation [37] Download Bibtex Eintrag [38]

Das variationale Dirichlet Prozess Mixture Modell [39]

Batz, P.

2010 HU Berlin

Link zur Publikation [40] Download Bibtex Eintrag [41]

Approximate inference for stochastic reaction processes [42]

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

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

Link zur Publikation [43] Download Bibtex Eintrag [44]

Approximate parameter inference in a stochastic reaction-diffusion model [45]

Ruttor, A. and Opper, M.

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

Link zur Publikation [46] Download Bibtex Eintrag [47]

Approximate inference in continuous time Gaussian-Jump processes [48]

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

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

Link zur Publikation [49] Download Bibtex Eintrag [50]

Comparing diffusion and weak noise approximations for inference in reaction models [51]

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

Link zur Publikation [52] Download Bibtex Eintrag [53]

MCMC for continuous time switching models [54]

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

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

Link zur Publikation [55] Download Bibtex Eintrag [56]

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster [57]

Opper, M., Dewar, M. A., Kadirkamanathan, V. and Sanguinetti, G.

BMC Systems Biology. BMC Systems Biology %, 669 - 676. 2010

Download Bibtex Eintrag [58]

A new variational radial basis function approximation for inference in multivariate diffusions [59]

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

Neurocomputing. Elsevier, 1186 - 1198. 2010

Download Bibtex Eintrag [60]

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