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Advanced Mean Field Methods: Theory and Practice [20]

||MIT Press. 2001

Download Bibtex entry [21]

A

Variational Inference for Diffusion Processes [22]

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

Advances in Neural Information Processing Systems 20. MIT Press, 17–24. 2008

Download Bibtex entry [23]

Gaussian Process Approximations of Stochastic Differential Equations [24]

Archambeau, C., Cornford, D., Opper, M. and Shawe-Taylor, J.

Journal of Machine Learning Research: Workshop and Conference Proceedings, 1:1–16. 2007

Link to publication [25] Download Bibtex entry [26]

Approximate inference for continuous–time Markov processes [27]

Archambeau, C. and Opper, M.

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

Link to publication [28] Download Bibtex entry [29]

The Variational Gaussian Approximation Revisited [30]

Archambeau, C. and Opper, M.

Neural Computation, 786-92. 2009

Download Bibtex entry [31]

B

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves [32]

Bachschmid-Romano, L. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing, P09016. 2015

Download Bibtex entry [33]

A statistical physics approach to learning curves for the inverse Ising problem [34]

Bachschmid-Romano, L. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, 063406. 2017

Link to publication [35] Download Bibtex entry [36]

Perturbative Black Box Corrected Variational Inference [37]

Bamler R., C. Z. O. M. M. S.

Advances in Neural Information Processing Systems 30. IEEE, 11. 2017

Link to publication [38] Download Bibtex entry [39]

Das variationale Dirichlet Prozess Mixture Modell [40]

Batz, P.

2010 HU Berlin

Link to publication [41] Download Bibtex entry [42]

Parameter Estimation for Stochastic Reaction Processes using Sequential Monte Carlo Methods [43]

Batz, P.

2010 HU Berlin

Link to publication [44] Download Bibtex entry [45]

Variational estimation of the drift for stochastic differential equations from the empirical density [46]

Batz, P., Ruttor, A. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, 083404. 2016

Link to publication [47] Download Bibtex entry [48]

Approximate Bayes learning of stochastic differential equations [49]

Batz, P., Ruttor, A. and Opper, M.

Phys. Rev. E, 022109. 2018

Download Bibtex entry [50]

Classification of task related fRMI: a complex network analysis [51]

Bey, P.

2014

Link to publication [52] Download Bibtex entry [53]

Construction Algorithm for the Parity Machine [54]

Biehl, M. and Opper, M.

Physica A, 307–313. 1993

Download Bibtex entry [55]

Tilinglike Learning in the Parity Machine [56]

Biehl, M. and Opper, M.

Phys. Rev. A, 6888–6894. 1991

Download Bibtex entry [57]

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