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Publikationsliste

N


The Variational Gaussian Approximation Revisited

Archambeau, C. and Opper, M.

Neural Computation, 786-92. 2009

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Sparse On-Line Gaussian Processes

Csató, L. and Opper, M.

Neural Computation, 641 - 668. 2002

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M

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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J

Bayesian Analysis of the Scatterometer Wind Retrieval inverse Problem: Some new Approaches

Cornford, D., Csató, L., Evans, D. J. and Opper, M.

Journal Royal Statistical Society B, 1–17. 2004

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Inferring hidden states in a random kinetic Ising model: replica analysis

Romano, L. B. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, P06013. 2014

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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

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Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding

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

Journal of Statistical Mechanics: Theory and Experiment, P03009. 2013

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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

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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

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Approximate inference techniques with expectation constraints

Heskes, T., Opper, M., Wiegerinck, W., Winther, O. and Zoeter, O.

Journal of Statistical Mechanics (JSTAT) 2005

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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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Expectation propagation for continuous time stochastic processes

Cseke, B., Schnoerr, D., Opper, M. and Sanguinetti, G.

Journal of Physics A: Mathematical and Theoretical. IOPscience, 494002. 2016

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