1. Data assimilation - the collaborative research centre SFB1294
  2. Open Science
  3. Publications

Publications

  • Spokoiny, V. (2019). Bayesian inference for nonlinear inverse problems. arXiv:1912.12694

  • Cervantes, S., Shprits, Y. Y., Aseev, N., Drozdov, A., Castillo, A., and Stolle, C. (2020). Identifying radiation beltelectron source and loss processes by assimilating spacecraft data in a three-dimensional diffusionmodel. J. Geophys. Res.-Space, 125(1):1–16, doi:10.1029/2019JA027514

  • Engbert, R., Rabe, M.M., Seelig, S.A., & Reich, S. (2019). Bayesian parameter estimation for dynamical models of eye-movement control using adaptive Markov Chain Monte Carlo simulations. Forschung im HLRN-Verbund 2019.

  • Gugushvili, S., Mariucci, E. and Meulen, van der F. (2019). Decompounding discrete distributions: A non-parametric Bayesian approach. To appear in Scandinavian Journal of Statistics. arXiv: 1903.11142 

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

  • Pathiraja, S. and Reich, S. (2019). Discrete gradients for computational Bayesian inference. Journal of Computational Dynamics, 6, 385-400. arXiv:1901.06300; doi: 10.3934/jcd.2019019

  • Geßner, H. & Kiy, A., (2019). A mobile campus application as a sensor node for Personal Learning Environments. In: Pinkwart, N. & Konert, J. (Hrsg.), DELFI 2019. Bonn: Gesellschaft für Informatik e.V.. (S. 187-192). DOI: 10.18420/delfi2019_356

  • Lange, T. and Stannat, W. (2019): On the continuous time limit of Ensemble Square Root Filters. arXiv 1910.12493

  • Spokoiny, V., and Panov, M. (2019). Accuracy of Gaussian approximation in nonparametric Bernstein–vonMises theorem. arXiv:1910.06028

  • Blanchard, G. and Zadorozhnyi, O. (2019). Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods. Bernoulli, 25(4B), 3421-3458. doi:10.3150/18-BEJ1095 (arXiv: 1712.01934)

  • Castillo, A. M., Shprits, Y. Y., Ganushkina, N., Drozdov, A., Aseev, N., Wang, D. and Dubyagin, S. (2019). Simulations of the inner magnetospheric energetic electrons using the IMPTAM-VERB coupled model. Journal of Atmospheric and Solar-Terrestrial Physics. doi: 10.1016/j.jastp.2019.05.014 

  • Malem-Shinitski, N., Seelig, S. A., Reich, S. and Engbert, R. (2019). Bayesian inference for an exploration-exploitation model of human gaze control. Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany (extended abstract). doi:10.32470/CCN.2019.1246-0

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Reich, S., Engbert, R. (2019). Parameter estimation for the SWIFT model of eye-movement control during reading. Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany (extended abstract) doi:10.32470/CCN.2019.1369-0

  • Shcherbakov, R., Zhuang, J., Zöller, G. and Ogata, Y. (2019). Forecasting the magnitude of the largest expected earthquake, Nature Communications, 10, nr. 4051. doi: 10.1038/s41467-019-11958-4

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

  • Houdebert, P. (2019). Phase transition of the non-symmetric Continuum Potts model. arXiv: 1908.10066

  • Avanesov, V. (2019). How to gamble with non-stationary X-armed bandits and have no regrets. arXiv:1908.07636

  • Avanesov, V. (2019). Structural break analysis in high-dimensional covariance structure. arXiv: 1803.00508

  • Cvetković N., Conrad T., and Lie H.C. (2019). Convergent discretisation schemes for transition path theory for diffusion processes (2019). SIAM Multiscale Modelling and Simulation 19(1), 242–266. doi.org/10.1137/20M1329354; arXiv:1907.05799

  • Avanesov, V. (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603

  • Götze, F., Naumov, A., Spokoiny, V. and Ulyanov, V. (2019). Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements, Bernoulli, in print. arXiv:1708.08663

  • Lefakis, L., Zadorozhnyi, O. and Blanchard, G. (2019). Efficient Regularized Piecewise-Linear Regression Trees. arXiv: 1907.00275​​​​​​​ 

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

  • 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. Neural Computation Vol. 31(10), 2004-2024. doi:10.1162/neco_a_01224. arXiv:1902.05062

  • Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2019). Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing, Journal of Vision, 19(6):5, 1-19. doi: 10.1167/19.6.5

  • 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

  • Lontsi, A. M., García-Jerez, A., Molina-Villegas, J. C., Sánchez-Sesma, F. J., Molkenthin, C., Ohrnberger, M., Krüger, F., Wang, R. and Fäh, D. (2019). A generalized theory for full microtremor horizontal-to-vertical [H/V(z, f)] spectral ratio interpretation in offshore and onshore environments, Geophysical Journal International, 218(2), 1276–1297. doi: 10.1093/gji/ggz223 arXiv: 1907.04606 

  • Achddou, J., Lam-Weil, J., Carpentier, A. and Blanchard, G. (2019). A minimax near-optimal algorithm for adaptive rejection sampling. Proceedings of the 30th International Conference on Algorithmic Learning Theory, PMLR 98:94-126, 2019. Open Access

  • Reich, S. (2019). Data assimilation: The Schrödinger perspective. Acta Numerica, 28, 635-711. arXiv:1807.08351; doi:10.1017/S0962492919000011

  • Locatelli, A., Carpentier, A., and Valko, M. (2019). Active multiple matrix completion with adaptive confidence sets. Proceedings of Machine Learning Research, PMLR, 89, 1783-1791. Open Access

  • Seznec, J, Locatelli, A., Carpentier, A., Lazaric, A., and Valko, M. (2019). Rotting bandits are no harder than stochastic ones. Proceedings of Machine Learning Research, in PMLR 89:2564-2572. Open Access

  • 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., 145, 2335-2365. arXiv: 1807.10434v2 doi: 10.1002/qj.3551

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

  • Katz-Samuels, J., Blanchard, G. and Scott, C. (2019). Decontamination of Mutual Contamination Models. Journal of Machine Learning Research (41):1−57, 2019 Open Access

  • Carpentier, A., Duval, C. and Mariucci, E. (2019). Total variation distance for discretely observed Lévy processes: a Gaussian approximation of the small jumps. arXiv: 1810.02998

  • Aseev, N. A. and Shprits, Y. Y. (2019). Reanalysis of ring current electron phase space densities using Van AllenProbe observations, convection model, and log-normal Kalman Filter. Space Weather, 17(4):619–638, doi:10.1029/2018SW002110

  • Salamat, M., Zöller, G. and Amini, M. (2019). Prediction of the Maximum Expected Earthquake Magnitude in Iran: From a Catalog with Varying Magnitude of Completeness and Uncertain Magnitudes, Pure and Applied Geophysics, 176 (8): 3425–3438. doi: 10.1007/s00024-019-02141-3

  • 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

  • Opper, M. (2019). Variational inference for stochastic differential equations. Ann. Phys., 531(3):1800233, doi:10.1002/andp.201800233

  • Duval, C. and Mariucci, E. (2019). Compound Poisson approximation to estimate the Lévy density. arXiv: 1702.08787 

  • Aseev, N. A., Shprits, Y. Y., Wang, D., Wygant, J., Drozdov, A. Y., Kellerman, A. C., and Reeves, G. D. (2019). Transport and loss of ring current electrons inside geosynchronous orbit during the 17 March 2013 storm. J. Geophys. Res.-Space, 124(2):915–933. doi:10.1029/2018JA026031

  • Blanchard, G., Neuvial, P. and Roquain, E. (2019). Post hoc inference via joint family-wise error rate control. (to appear in Annals of Statistics) arXiv: 1703.02307