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

Publications

  • Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: II. convergence of Onsager–Machlup functionals: II. Infinite product measures on Banach spaces. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f82.

  • M. Redmann, M.A. Freitag (2021). Optimization based model order reduction for stochastic systems. Appl. Math. Comput., 398.

  • H. C. Lie, M. Stahn, T.J. Sullivan (2022). Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, Volume 59, Number 13 doi:10.1007/s10092-022-00457-6.

  • M.A. Freitag, S. Reich (2022). Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen. Mitteilungen der Deutschen Mathematiker-VereinigungVerlag, De GruyterSeiten, 108‒112, Band 30.

  • Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Gradient flows for sampling: Mean-field models, Gaussian approximations and affine invariance arXiv:2302.11024

  • Cvetkovic, N. and Lie, H. C. and Bansal, H. and Veroy-Grepl, K. (2023). Choosing observation operators to mitigate model error in Bayesian inverse problems. ArXiv 2301.04863

  • Kim, J.W. and Reich, S. (2023): On forward-backward SDE approaches to continuousßtime minimum variance estimation. arXiv 2304.12727

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang., S. (2023). Infinite-dimensional diffusion models for function spaces arXiv:2302.10130

  • Mach, T and Freitag, M.A. (2023). Solving the Parametric Eigenvalue Problem by Taylor Series and Chebyshev Expansion arXiv 230212.03661

  • Rabe, M. M., Paape, D., Mertzen, D., Vasishth, S., & Engbert, R. (2023). SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading. arXiv:2303.05221

  • Janák, J. and Reiß, M. (2023). Parameter estimation for the stochastic heat equation with multiplicative noise from local measurements. arXiv:2303.00074v1

  • Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2022). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. arXiv: 2207.03954

  • Moldenhawer, T., Moreno, E., Schindler, D., Flemming, S., Holschneider, M., Huisinga, W., Alonso, S. and Beta, C. (2022). Spontaneous transitions between amoeboid and keratocyte-like modes of migration. Front. Cell Dev. Biol., 10:898351, doi:10.3389/fcell.2022.898351

  • Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2022). Three-component contour dynamics model to simulate and analyze amoeboid cell motility. arXiv:2210.12978

  • Yochelis, A., Flemming, S. and Beta, C. (2022) Versatile Patterns in the Actin Cortex of Motile Cells: Self-Organized Pulses Can Coexist with Macropinocytic Ring-Shaped Waves. Phys. Rev. Lett., 129:088101, doi:10.1103/PhysRevLett.129.088101

  • Schwetlick, L.; Backhaus, D. & Engbert, R. (2022). A dynamical scan-path model for task-dependence during scene viewing. Psychological Review, American Psychological Association (APA),doi: 10.1037/rev0000379

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering II: Optimal Control. arXiv 2208.06587

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering I: Observability. arXiv 2208.06586

  • König, J. and Freitag, M. (2022). Time-limited Balanced Truncation for Data Assimilation Problems. arXiv 2212.07719.

  • Vilk, O., Aghion, E., Avgar, T., Beta, C., Nagel, O., Sabri, A., Sarfati, R., Schwartz, D., Weiss, M., Krapf, D., Nathan, R., Metzler, R. and Assaf, M. (2022) Unravelling the origins of anomalous diffusion: From molecules to migrating storks. Phys. Rev. Research, 4:033055, doi:10.1103/PhysRevResearch.4.033055

  • Lie, H. C. and Rudolf, D. and Sprungk, B. and Sullivan, T. J. (2022). Dimension-independent Markov chain Monte Carlo on the sphere. ArXiv 2112.12185

  • Riedel, C., Geßner, H., Seegebrecht, A., Ayon, S. I., Chowdhury, S. H., Engbert, R. & Lucke, U., (2022). Including Data Management in Research Culture Increases the Reproducibility of Scientific Results. In: Demmler, D., Krupka, D. & Federrath, H. (Hrsg.), INFORMATIK 2022. Gesellschaft für Informatik, Bonn. (S. 1341-1352). DOI: 10.18420/inf2022_114

  • Moreno, E., Grossmann, R., Beta, C. and Alonso S. (2022) From Single to Collective Motion of Social Amoebae: A Computational Study of Interacting Cells. Front. Phys., 9:750187, doi:10.3389/fphy.2021.750187

  • Reich, S. (2022): Data assimilation: A dynamic homotopy-based coupling approach. arXiv 2209.05279

  • Calvello, E., Reich, S. and Stuart A.M.(2022): Ensemble Kalman methods: A mean field approach. arXiv 2209.11371

  • Alqahtani, A., Mach, T., and Reichel, L. (2023). Solution of Ill-posed Problems with Chebfun. Numerical Algorithms (2023). doi:10.1007/s11075-022-01390-z arXiv 2007.16137

  • Pidstrigach, J. and Reich, S. (2022). Affine-invariant ensemble transform methods for logistic regression. Foundation of Computational Mathematics, 22, doi:10.10007/s10208-022-09550-2.

  • Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2022): GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. Statistics and Computation, Vol. 32, 29. doi:10.1007/s11222-022-10085-3.

  • Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. arXiv:2204.04386.

  • Yadav, H., Smith, G., Reich, S., and Vasishth, S. (2022). Number feature distortion modulates cue-based retrieval in reading. doi:10.31234/osf.io/s4c9t.

  • Engbert, R., Rabe, M. M., Schwetlick, L., Seelig, S. A., Reich, S., Vasishth, S. (2022). Data assimilation in dynamical cognitive science. Trends in Cognitive Sciences, 26(2), 99-102, doi:10.1016/j.tics.2021.11.006.

  • Malem-Shinitski, N., Ojeda, C., and Opper, M. (2022). Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy, 24(3), 356, doi: 10.3390/e24030356.

  • Mach, T., Reichel, L., and Van Barel, M. (2023). Adaptive cross approximation for Tikhonov regularization in general form. Numerical Algorithms, doi:10.1007/s11075-022-01395-8 arXiv 2204.05740

  • Gaudlitz, S. and Reiß, M. (2022). Estimation for the reaction term in semi-linear SPDEs under small diffusivity. arXiv:2203.10527

  • Pathiraja, S., & van Leeuwen, P. J. (2022). Multiplicative non-Gaussian model error estimation in data assimilation. Journal of Advances in Modeling Earth Systems, 14, e2021MS002564. https://doi.org/10.1029/2021MS002564

  • Ruchi, S., Dubinkina, S. and de Wiljes, J. (2021). Fast hybrid tempered ensemble transform filter for Bayesian elliptical problems via Sinkhorn approximation. Nonlinear Processes in Geophysics, 28(1): 23-41 [1]

  • Reich, S. (2022). Frequentist perspective on robust parameter estimation using the ensemble Kalman filter arXiv:2201.000611

  • Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: I. With applications to maximum a posteriori estimation in Bayesian inverse problems. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f81.

  • Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior sampling, Computational Geosciences, https://doi.org/10.1007/s10596-021-10100-y arXiv:2101.03612

  • Pathiraja, S., Reich, S., Stannat, W. (2021): McKean-Vlasov SDEs in nonlinear filtering. SIAM Journal on Control and Optimization.  doi:10.1137/20M1355197 arXiv 2007.12658

  • Gottwald, G.A., and Reich, S. (2021). Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 31, 101103, doi:10.1063/5.0066080 arXiv:2108.03561

  • Pidstrigach, J. and Reich, S. (2021). Affine-invariant ensemble transform methods for logistic regression arXiv: 2104.08061

  • Pathiraja, S. and Stannat, W. (2021). Analysis of the feedback particle filter with diffusion map based approximation of the gain. Foundations of Data Science. doi:10.3934/fods.2021023 arXiv:2109.02761

  • Schindler, D., Moldenhawer, T., Stange, M., Lepro, V., Beta, C., Holschneider, M., and Huisinga, W. (2021). Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows. PLoS Comput Biol 17(8): e1009268. doi:journal.pcbi.1009268

  • Geßner, H. (2021). Transparently Safeguarding Good Research Data Management with the Lean Process Assessment Model. In: E-Science-Tage 2021: Share Your Research Data. Heidelberg. DOI: 10.11588/heidok.00029719

  • Dietrich, F., Makeev, A., Kevrekidis, G., Evangelou, N., Bertalan, T., Reich, S., and Kevrekidis, I.G. (2021). Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning. arXiv:2106.09004

  • Rabe, M. M., Chandra, J., Krügel, A., Seelig, S. A., Vasishth, S., & Engbert, R. (2021). A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts. Psychological Review doi:10.1037/rev0000268, psyarXiv

  • Pasemann, G. and Flemming, S. and Alonso, S. and Beta, C. and Stannat, W. (2020). Diffusivity Estimation for Activator-Inhibitor Models: Theory and Application to Intracellular Dynamics of the Actin Cytoskeleton. Journal of Nonlinear Science 31, 59 (2021). doi:10.1007/s00332-021-09714-4  arXiv 2005.09421

  • Gottwald, G., and Reich, S. (2021). Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation. Physica D, Vol. 423, 132911. doi:10.1016/j.physd.2021.132911. arXiv:2007.07383

  • Wormell, C.L. and Reich, S. (2021). Spectral convergence of diffusion maps: Improved error bounds and an alternative normalisation. SIAM Journal Numerical Analysis,59, 1687-1734. arXiv 2006.02037; doi:10.1137/30M1344093

  • Hartung, N., Wahl, M., Rastogi, A., and Huisinga, W. (2021). Nonparametric goodness-of-fit tests for parametric covariate models in pharmacometric analyses. CPT Pharmacometrics & Systems Pharmacology 10: 564-576. ArXiv 2011.07539 DOI

  • Cialenco, I. and Kim, H.-J. and Pasemann, G. (2021). Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach. arXiv:2103.04211

  • Blanchard, G., Deshmukh, A., Dogan, U., Lee, G. and Scott, C. (2021). Domain Generalization by Marginal Transfer Learning. Journal of Machine Learning Research 22(2):1−55. Open Access

  • Zadorozhnyi, O. and Gaillard, P. and Gerchinovitz, S. and Rudi, A. (2021). Online nonparametric regression with Sobolev kernels. arxiv: 2102.03594

  • Lange, T. and Stannat W. (2021). Mean field limit of Ensemble Square Root filters - discrete and continuous time, Foundations of Data Science. doi: 10.3934/fods.2021003

  • Reich, S. and Weissmann, S. (2021). Fokker-Planck particle systems for Bayesian inference: Computational approaches, SIAM/ASA J. Uncertainty Quantification, 9(2), 446–482. doi: 10.1137/19M1303162;  arXiv:1911.10832

  • Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior sampling.  arXiv:2101.03612

  • Lange, T. (2020): Derivation of Ensemble Kalman-Bucy Filters with unbounded nonlinear coefficients. arXiv 2012.07572

  • Schwetlick, L., Rothkegel, L.O.M., Trukenbrod, H.A., Engbert, R. (2020). Modeling the effects of perisaccadic attention on gaze statistics during scene viewing. Communications Biology, 3, 727. doi: 10.1038/s42003-020-01429-8

  • Moreno, E., Flemming, S., Font, F., Holschneider, M., Beta, C., and Alonso, S. (2020). Modeling cell crawlingstrategies with a bistable model: From amoeboid to fan-shaped cell motion. Physica D, 412:132591, doi:10.1016/j.physd.2020.132591

  • Pathiraja, S. (2020): L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients. arXiv 2011.13009

  • Castillo, A. M., de Wiljes, J., Shprits, Y. Y., and Aseev, N. A. (2020). Reconstructing the dynamics of the outerelectron radiation belt by means of the standard and ensemble Kalman filter with the VERB-3Dcode, ESSOAr. doi:10.1002/essoar.10504674.

  • Makowski, S., Jäger, L. A., Prasse, P., & Scheffer, T. (2020). Biometric identification and presentation-attack detection using micro- and macro-movements of the eyes. International Joint Conference on Biometrics (IJCB), in press. Preprint: [1]

  • Prasse, P., Jäger, L. A., Makowski, S., Feuerpfeil, M., & Scheffer, T. (2020). On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. Procedia Computer Science, 176, 2088-2097. doi: 10.1016/j.procs.2020.09.245

  • Makowski, S., Jäger, L. A., Schwetlick, L., Trukenbrod, H., Engbert, R., & Scheffer, T. (2020). Discriminative Viewer Identification using Generative Models of Eye Gaze. Procedia Computer Science, 176, 1348-1357. doi: 10.1016/j.procs.2020.09.144

  • Lange, T. and Stannat, W. (2020): On the continuous time limit of the Ensemble Kalman Filter. Mathematics of Computation, 40(327), 233-265. arXiv 1901.05204v1; doi:10.1090/mcom/3588

  • Seelig, S., Risse, S., and Engbert, R. (2020). Predictive modeling of the influence of parafoveal informationprocessing on eye guidance in reading.  doi:10.31234/osf.io/vbmqn

  • Saggioro, E., de Wiljes, J., Kretschmer, M., and Runge, J. (2020). Reconstructing regime-dependent causalrelationships from observational time series. Chaos, 30(11):113115–1–113115–22, doi:10.1063/5.0020538

  • Engbert, R. (2021). Dynamical Models in Neurocognitive Psychology. Computational Approaches to Cognitionand Perception. Springer Nature. (in press)

  • Holschneider, M., Ferrat, K., Zöller, G., Molkenthin, C., and Hainzl, S. (2020). Richter b-value maps from local moments of seismicity.  arXiv:2010.12298

  • Altmeyer, R. and Reiß, M. (2020). Nonparametric estimation for linear SPDEs from local measurements. Annals of Applied Probability, to appear. arXiv 1903.06984

  • Altmeyer, R. and Bretschneider, T. and Janák, J. and Reiß, M. (2020). Parameter Estimation in an SPDE Model for Cell Repolarisation. arXiv 2010.06340

  • M. Stange, T. Moldenhawer, and C. Beta (2020). Fluorescent (C)LSM image sequences of dictyostelium discoideum (Ax2-LifeAct mRFP) for cell track and cell contour analysis. doi:10.5061/dryad. b5mkkwhbd.

  • Gaidzik, F., Pathiraja, S., Saalfeld, S., Stucht, D., Speck, O., Thevenin, D., Janiga, G. (2020). Hemodynamic Data Assimilation in a Subject-specific Circle of Willis Geometry. Clinical Neuroradiology, doi:10.1007/s00062-020-00959-2

  • Reich, S., and Rozdeba, P. J. (2020). Posterior contraction rates for non-parametric state and drift estimation. Foundation of Data Science, Vol. 2, 333-349. doi:10.3934/fods.2020016. arXiv:2003.09219

  • Houdebert, P., Zass, A. (2020), An explicit continuum Dobrushin uniqueness criterion for Gibbs point processes with non-negative pair potentials. arxiv 2009.06352

  • Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2020). GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. arXiv:2005.12857

  • Malem-Shinitski, N., Opper, M., Reich, S., Schwetlick, S., Seelig S. A., & Engbert, R (2020). A Mathematical Model of Exploration and Exploitation in Natural Scene Viewing. PLoS Computational Biology. doi:10.1371/journal.pcbi.1007880

  • Rastogi, A. (2020). Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems. Communications on Pure & Applied Analysis 19(8): 4111-4126. ArXiv 2002.01303DOI

  • Rastogi, A., Blanchard, G. and Mathé, P. (2020). Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems. Electronic Journal of Statistics 14(2): 2798-2841. ArXiv 1902.05404v2DO

  • Milbradt, C. and Wahl, M. (2020). High-probability bounds for the reconstruction error of PCA. Statist. Probab. Lett. 161. ArXiv 1909.10787

  • Reiß, M. and Wahl, M. (2020). Non-asymptotic upper bounds for the reconstruction error of PCA. Ann. Stat. 48(2): 1098-1123. arXiv 1609.03779

  • Rastogi, A. (2019). Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems. AIP Conference Proceedings 2183(1): 110004 AIP Publishing LLC. DOI

  • Rastogi, A. and Mathé, P. (2020). Inverse learning in Hilbert scales.arXiv 2002.10208

  • Celisse, A. and Wahl, M. (2020). Analyzing the discrepancy principle for kernelized spectral filter learning algorithms.arXiv: 2004.08436

  • D. Schindler, T. Moldenhawer, L. Lindenmeier, and M. Holschneider. Amoepy (version 1.0), 2020. doi:10.5281/zenodo.3982372

  • Cervantes, S., Shprits, Y. Y., Aseev, N. A., and Allison, H. J. (2020). Quantifying the effects of EMIC wavescattering and magnetopause shadowing in the outer electron radiation belt by means of data as-similation. J. Geophys. Res.-Space, 125(8):e2020JA028208, doi:10.1029/2020JA028208

  • de Wiljes, J. and Tong, X. T (2020). Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations. Nonlinearity, 33(9): 4752-4782 [2]  arXiv:1908.10580v3

  • Maoutsa, D., Reich, S., and Opper, M. (2020). Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation. Entropy, Vol. 22, 0802. doi:10.3390/e22080802. arXiv:2006.00702

  • Avanesov, V. (2020) Data-driven confidence bands for distributed nonparametric regression. Proceedings of Machine Learning Research vol. 125. DSpace,  PMLR

  • Zass, A. (2020). A Gibbs point process of diffusions: existence and uniqueness. Proceedings of the XI international conference stochastic and analytic methods in mathematical physics (Lectures in pure and applied mathematics 6), Universitätsverlag Potsdam, 13-22. Open Access

  • Houdebert, P. (2020). Numerical study for thephase transition of thearea-interaction model. Proceedings of the XI international conference stochastic and analytic methods in mathematical physics (Lectures in pure and applied mathematics 6), Universitätsverlag Potsdam, 165–174. Open Access

  • Ruchi, A., Dubinkina, S., and de Wiljes, J. (2020). Fast hybrid tempered ensemble transform filter for Bayesianelliptical problems. Nonlin. Processes Geophys., in press, doi:10.5194/npg-2020-24

  • Hamm, M., Pelivan, I., Grott, M., and de Wiljes, J. (2020). Thermophysical modelling and parameter esti-mation of small solar system bodies via data assimilation. Mon. Not. R. Astron. Soc., 496:2776–2785, doi:10.1093/mnras/staa1755

  • Maneugueu, A., Vernade, C., Carpentier A., and Valko, M. (2020). Stochastic bandits with arm-dependent delays. In Thirty-seventh International Conference on Machine Learning, accepted for publication. arXiv:2006.10459

  • Maier C., Hartung N., Kloft C., Huisinga W., de Wiljes J. (2020): Combining reinforcement learning with data assimilation for individualised dosing policies in oncology. arXiv:2006.01061

  • Roelly, S. and Zass, A. (2020). Marked Gibbs Point Processes with Unbounded Interaction: An Existence Result. Journal of Statistical Physics 179, 972–996 (2020). Open Access

  • Garbuno-Inigo, A., Nüsken, N., and Reich, S. (2020). Affine invariant interacting Langevin dynamics for Bayesian inference. SIAM J. Dyn. Syst., Vol. 19(3), 1633-1658. doi:10.1137/19M1304891. arXiv:1912.02859

  • Pasemann, G. and Stannat, W. (2019). Drift Estimation for Stochastic Reaction-Diffusion Systems. Electron. J. Statist. 14 (2020), no. 1, 547-579. doi:10.1214/19-EJS1665  arXiv 1904.04774

  • Altmeyer, R. and Cialenco, I. and Pasemann, G. (2020). Parameter estimation for semilinear SPDEs from local measurements. arXiv 2004.14728

  • Jäger, L. A., Makowski, S., Prasse, P., Liehr, S., Seidler, M., & Scheffer, T. (2019). Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 299-314. Springer, Cham. doi: 10.1007/978-3-030-46147-8_18

  • Zhelavskaya, I., Aseev, N. A., Shprits, Y. Y., and Spasojevi, M. (2020). A combined neural network- and physics-based approach for modeling the plasmasphere dynamics, ESSOAr. doi:10.1002/essoar.10502691.1

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Risse, S., Reich, S., and Engbert, R. (2020). Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. Journal of Mathematical Psychology, 95, 102313. doi:10.1016/j.jmp.2019.102313; arXiv: 1901.11110.

  • Flemming, S., Font, F., Alonso, S., and Beta, C. (2020). How cortical waves drive fission of motile cells. PNAS, 117(12):6330–6338, doi:10.1073/pnas.1912428117

  • Duval, C. and Mariucci, E. (2020). Non-asymptotic control of the cumulative distribution function of Lévy processes. arXiv 2003.09281

  • Vernade, C., Carpentier, A., Lattimore, T., Zappella, G., Ermis, B. and Brueckner, M. (2020). Linear Bandits with Stochastic Delayed Feedback. arXiv:1807.02089

  • de Wiljes, J., Pathiraja, S. and Reich, S. (2020). Ensemble transform algorithms for nonlinear smoothing problems. SIAM J. Scientific Computing, 42, A87-A114. arXiv:1901.06300; doi: 10.1137/19M1239544

  • Maier C., Hartung N., de Wiljes J., Kloft C. and Huisinga W.. Bayesian data assimilation to supportinformed decision-making in individualized chemotherapy. CPT Pharmacometrics Syst. Pharmacol.,9(3):153164, 2020. doi:10.1002/psp4.12492

  • 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

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