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  • Mariucci, E., Ray, K. and Szabó, B. (2019). A Bayesian nonparametric approach to log-concave density estimation. To appear in Bernoulli. arXiv: 1703.09531

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  • Malem-Shinitski, N., Seelig, S. A., Reich, S. and Engbert, R. (2019). Bayesian inference for an exploration-exploitation model of human gaze control. Conference extended abstract. (manuscript)

  • Nuesken, N. and Reich, S. (2019). Note on Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and StuartarXiv:1908.10890

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  • Zadorozhnyi, O., Blanchard, G. and Carpentier, A. (2019). Restless dependent bandits with fading memory. arXiv: 1906.10454 

  • Pathiraja, S. and Reich, S. (2019). Discrete gradients for computational Bayesian inference. Journal of Computational Dynamics (in press) arXiv:1901.06300

  • Ty, A.J.A., Fang, Z., Gonzales, R.A., Rozdeba, P.J. and Abarbanel, H.D.I. (2019). Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing. arXiv:1902.05062

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  • Blanchard, G., Mathé, P. and Mücke, N. (2019). Lepskii Principle in Supervised Learning. arXiv: 1905.10764 

  • Somogyvári, M. and Reich, S. (2019). Convergence tests for transdimensional Markov chains in geoscience imaging, Math Geosci, 2019. doi: 10.1007/s11004-019-09811-x

  • Nüsken, N., Reich, S. and Rozdeba, P. J. (2019). State and parameter estimation from observed signal increments, Entropy, Vol. 21(5), 505. arXiv:1903.10717 ;  doi: 10.3390/e21050505

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  • Achdou, J., Lam, J., Carpentier, A. and Blanchard, G. (2019). A minimax near-optimal algorithm for adaptive rejection sampling. Accepted for ALT'2019. Arxiv 1810.09390

  • Reich, S. (2019). Data assimilation: The Schrödinger perspective. Acta Numerica, Vol. 28, pp. 635-710. arXiv: 1807.08351

  • Leeuwen, P. J. v., Künsch, H.-R., Nerger, L., Potthast, R. and Reich, S. (2019). Particle filters for high-dimensional geoscience applications: a review. Quarterly J Royal Meteorlog. Soc., published online 22 April 2019. arXiv: 1807.10434v2

  • Wahl, M. (2019). A note on the prediction error of principal component regression.arXiv: 1811.02998

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  • Altmeyer, R. and Reiß, M. (2019). Nonparametric estimation for linear SPDEs from local measurements.arXiv: 1903.06984

  • Rastogi, A., Blanchard, G. and Mathé, P. (2019). Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems.arXiv 1902.05404

  • Rothkegel, L. O., Schütt, H. H., Trukenbrod, H. A., Wichmann, F. A. and Engbert, R. (2019). Searchers adjust their eye-movement dynamics to target characteristics in natural scenes. Scientific Reports, 9, article no. 1635. doi: 10.1038/s41598-018-37548-w

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Risse, S., Reich, S., and Engbert, R. (2019). Bayesian parameter estimation for the SWIFT model of eye-movement control during readingarXiv: 1901.11110.

  • Schneider, S. and Heinecke, L. (2019). The need to transform Science Communication from being multi-cultural via cross-cultural to intercultural, Adv. Geosci., 46, 11-19, doi: 10.5194/adgeo-46-11-2019 

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  • Blanchard, G. and Mücke, N. (2018). Parallelizing Spectral Algorithms for Kernel Learning. Journal of Machine Learning Research (30):1-29, 2018. Open Access

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  • Zöller, G. (2018). A Statistical Model for Earthquake Recurrence Based on the Assimilation of Paleoseismicity, Historic Seismicity, and Instrumental Seismicity. Journal of Geophysical Research: Solid Earth, 123, 4906-4921. doi: 10.1029/2017JB015099

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  • de Wiljes, J., Acevedo, W. and Reich, S. (2017). A second-order accurate ensemble transform particle filter. SIAM Journal on Scientific Computing, 39, A1834-A1850. arXiv: 1608.08179doi: 10.1137/16M1095184

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