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Variational Inference for Diffusion Processes

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

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

Gaussian Process Approximations of Stochastic Differential Equations

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

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

Approximate inference for continuous–time Markov processes

Archambeau, C. and Opper, M.

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

The Variational Gaussian Approximation Revisited

Archambeau, C. and Opper, M.

Neural Computation, 786-92. 2009

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

Bachschmid-Romano, L. and Opper, M.

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

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

Bachschmid-Romano, L. and Opper, M.

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

Perturbative Black Box Corrected Variational Inference

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

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

Das variationale Dirichlet Prozess Mixture Modell

Batz, P.

2010 HU Berlin

Parameter Estimation for Stochastic Reaction Processes using Sequential Monte Carlo Methods

Batz, P.

2010 HU Berlin

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

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

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

Approximate Bayes learning of stochastic differential equations

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

Phys. Rev. E, 022109. 2018

Classification of task related fRMI: a complex network analysis

Bey, P.

2014

Construction Algorithm for the Parity Machine

Biehl, M. and Opper, M.

Physica A, 307–313. 1993

Tilinglike Learning in the Parity Machine

Biehl, M. and Opper, M.

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