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Publikationsliste

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J

Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model [21]

Romano, L. B., Battistin, C., Opper, M. and Roudi, Y.

Journal of Physics A: Mathematical and Theoretical. IOPscience, 434003. 2016

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

Extended Plefka expansion for stochastic dynamics [24]

Bravi, B., Sollich, P. and Opper, M.

Journal of Physics A: Mathematical and Theoretical, 194003. 2016

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

A theory of solving TAP equations for Ising models with general invariant random matrices [27]

Opper, M., Cakmak, B. and Winther, O.

Journal of Physics A: Mathematical and Theoretical, 114002. 2016

Link zur Publikation [28] Download Bibtex Eintrag [29]

A comparison of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems [30]

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

Journal of Signal Processing Systems 2009

Link zur Publikation [31] Download Bibtex Eintrag [32]

Approximate inference techniques with expectation constraints [33]

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

Journal of Statistical Mechanics (JSTAT) 2005

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

A statistical physics approach for the analysis of machine learning algorithms on real data [36]

Malzahn, D. and Opper, M.

Journal of Statistical Mechanics (JSTAT) 2005

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

Inferring hidden states in a random kinetic Ising model: replica analysis [39]

Romano, L. B. and Opper, M.

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

Download Bibtex Eintrag [40]

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves [41]

Bachschmid-Romano, L. and Opper, M.

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

Download Bibtex Eintrag [42]

Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding [43]

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

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

Link zur Publikation [44] Download Bibtex Eintrag [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 zur Publikation [47] Download Bibtex Eintrag [48]

A statistical physics approach to learning curves for the inverse Ising problem [49]

Bachschmid-Romano, L. and Opper, M.

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

Link zur Publikation [50] Download Bibtex Eintrag [51]

Bayesian Analysis of the Scatterometer Wind Retrieval inverse Problem: Some new Approaches [52]

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

Journal Royal Statistical Society B, 1–17. 2004

Link zur Publikation [53] Download Bibtex Eintrag [54]

M

Optimal control as a graphical model inference problem [55]

Kappen, H. J., Gomez, V. and Opper, M.

Machine Learning. Springer, 159-182. 2012

Link zur Publikation [56] Download Bibtex Eintrag [57]

N

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models [58]

Ribeiro, F. and Opper, M.

Neural Computation. MIT Press, 1047–1069. 2011

Download Bibtex Eintrag [59]

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed–Form Approixmation [60]

Harel, Y., Meir, R. and Opper, M.

Neural Computation, 2056–2112. 2018

Download Bibtex Eintrag [61]

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