(very old photo)
Contact details:

I was a post-doc working with Guido Sanguinetti until 08/2017.

I have recently moved to a new job. Please contact me on the email address displayed above.


  • B. Cseke, D. Schnoerr, M. Opper and G. Sanguinetti
    Expectation propagation for diffusion processes by moment closure approximations
    [arxiv preprint]

  • B. Cseke and G. Sanguinetti
    Factored expectation propagation for input-output FHMM models in systems biology
    [arxiv preprint]


  • B. Cseke, A. Zammit-Mangion, T.Heskes and G. Sanguinetti,
    Sparse approximations in spatio-temporal point-process models
    Journal of American Statistical Association
    [arxiv version]

  • B. Cseke, M. Opper and G. Sanguinetti
    Approximate inference in latent Gaussian-Markov models from continuous time observations
    Neural Information Processing Systems, 2013

  • G. Schweikert, B. Cseke, T. Clouaire, A. Bird and G.Sanguinetti
    MMDiff: quantitative testing for shape changes in ChIP-Seq data sets
    BMC Genomics 2013, 14:826, 2013

  • B. Cseke and T. Heskes,
    Properties of Bethe free energies and message passing in Gaussian models
    Journal of Artificial Intelligence Research, vol. 41, 2011

  • B. Cseke and T. Heskes,
    Approximate marginals in latent Gaussian models
    Journal of Machine Learning Research, vol. 12, 2011

  • B. Cseke and T. Heskes,
    Improving posterior marginal approximations in latent Gaussian models
    Proceedings of the 13th Conference on Artificial Intelligence and Statistics, 2010

  • M. A. J. van Gerven, B. Cseke, F. de Lange and T. Heskes,
    Efficient Bayesian Multivariate fMRI Analysis using a Sparsifying Spatio-Temporal Prior
    Neuroimage, 2010

  • M. van Gerven, B. Cseke, R. Oostenveld and T. Heskes
    Bayesian Source Localization with the Multivariate Laplace Prior
    Advances in Neural Information Processing Systems 22, 2009

  • T. Heskes and B. Cseke
    Discussion of ``Approximate Bayesian inference for latent Gaussian models by using
    integrated nested Laplace approximations'' by H. Rue, S. Martino and N. Chopin

    Journal of the Royal Statistical Society Series B, 2009

  • E. Tsivtsivadze, B. Cseke and T. Heskes
    Kernel Principal Component Ranking: Robust Ranking on Noisy Data
    ECML/PKDD-Workshop on Preference Learning, 2009

  • B. Cseke and T. Heskes
    Bounds on the Bethe free energy for Gaussian networks
    Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, 2008

  • B. Cseke and L. Csato
    Multi-class inference with Gaussian processes
    Studia Univ. "Babes-Bolyai", Mathematica, vol. 1, nr. 3, 2005