Aktuelles Lehrangebot (Sommersemester 2024)

• Methods in Machine Learning
• Stochastische Simulation / Stochastis Simulation
• Versuchsplanung und multivariate Statistik

Allgemeines Lehrangebot

• Aktuelle Themen der Stochastik (Winter, jährlich)
• Mathematics of Machine Learning (Winter, jährlich)
• Methods in Machine Learning (Sommer, jährlich)
• Probabilistic Forecasting and Data Assimilation (Sommer, jährlich)
• Uncertainty Quantification (Winter, ungerade Jahre)
• Stochastic Methods for Material Science (Winter, jährlich)
• Versuchsplanung und multivariate Statistik (Sommer, jährlich)

• Unsicherheitsquantifizierung für Differentialgleichungen
• Hochdimensionale Approximationsmethoden
• Stochastische Simulationsverfahren, speziell Markowketten-Monte Carlo 
• Bayessche Inferenz für inverse Probleme
• Unsicherheiten im maschinelles Lernen

Google Scholar Profil

Preprints

• S. Power, D. Rudolf, B. Sprungk, A. Q. Wang (2024)
  Weak Poincaré inequality comparisons for ideal and hybrid slice sampling
  arXiv:2402.13678, 1-35.
   
• B. Sprungk, S. Weissmann, J. Zech (2023)
  Metropolis-adjusted interacting particle sampling
  arXiv:2312.13889, 1-40.
   
• M. Hasenpflug, D. Rudolf, B. Sprungk (2022)
  Wasserstein convergence rates of increasingly concentrating probability measures
  arXiv:2207.08551, 1-29.
   
• D. Rudolf, B. Sprungk (2022)
  Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors.
  arXiv:2202.12127, 1-27.

Begutachtete Publikationen

1. H. Höllwarth, S. A.H. Sander, M. Werner, S. Fuhrmann, B. Sprungk (2023)
   Simulation of phase separation in Na₂O-SiO₂ glasses under uncertainty
   Journal of Non-Crystalline Solids 621:122534 (7pp). 
    
2. Lie, H. C., Rudolf, D., Sprungk, B., Sullivan T. J. (2023)
   Dimension-independent Markov chain Monte Carlo on the sphere.
   Scandinavian Journal of Statistics 50(4):1818-1858. [arXiv]
    
3. Ernst, O. G., Pichler, A., Sprungk, B. (2022).
   Wasserstein sensitivity of Risk and Uncertainty Propagation.
   SIAM/ASA J. Uncertainty Quantification 10(3):915-948. [arXiv]
    
4. Eigel, M., Ernst, O., Sprungk, B., Tamellini, L. (2022)       
   On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion.
   SIAM J. Numer. Anal. 60(2):659-687 [arXiv]
    
5. Ernst, O. G., Sprungk, B., Tamellini, L. (2022).       
   On Expansions and Nodes for Sparse Grid Collocation of Lognormal Elliptic PDEs.
   In: H.-J. Bungartz et al. (Eds.) Sparse Grids and Applications - Munich 2018, Lecture Notes in Computational Science and Engineering, Band 144, Springer Cham, pp. 1-31. [arXiv]
    
6. Natarovskii, V., Rudolf, D., Sprungk, B. (2021)
   Geometric convergence of elliptical slice sampling.
   Proceedings of the 38th International Conference on Machine Learning, PLMR 139:7969-7978 [arXiv]
    
7. Klebanov, I., Sprungk, B., Sullivan, T. J. (2021)
   The linear conditional expectation in Hilbert space.
   Bernoulli 27(4):2267-2299. [arXiv]
    
8. Natarovskii, V., Rudolf, D., Sprungk, B. (2021)
   Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling.
   Ann. Appl. Probab. 31(2):806-825. [arXiv]
    
9. Habeck, M., Rudolf, D., Sprungk, B. (2020)
   Stability of doubly-intractable distributions.
   Electron. Commun. Probab. 25, paper no. 62, 13pp. [arXiv]
    
10. Schillings, C., Sprungk, B., Wacker, P. (2020)
    On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems.
    Numerische Mathematik 145:915-971. [arXiv]
     
11. Rudolf, D., Sprungk, B. (2020)
    On a Metropolis-Hastings importance sampling estimator
    Electron. J. Statist. 14(1):857-889. [arXiv]
     
12. Sprungk, B. (2020)
    On the Local Lipschitz Robustness of Bayesian Inverse Problems.
    Inverse Problems 36:055015 (31pp). [arXiv]
     
13. Ernst, O. G., Sprungk, B., Tamellini, L. (2018)
    Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables.
    SIAM J. Numer. Anal. 56(2):877-905. [arXiv]
     
14. Rudolf, D., Sprungk, B. (2018)
    On a Generalization of the Preconditioned Crank-Nicolson Metropolis Algorithm.
    Found. Comput. Math. 18:309-343. [arXiv]
     
15. Rudolf, D., Sprungk, B. (2017)
    Metropolis-Hastings Importance Sampling Estimator.
    Proc. Appl. Math. Mech. 17:731-734.
     
16. Hundt, S., Sprungk, B., Horsch, A. (2017)
    The Information Content of Credit Ratings: Evidence from European Convertible Bond Markets.
    The European Journal of Finance 23(14):1414-1445.
     
17. Ernst, O. G., Sprungk, B., Starkloff, H.-J. (2015)
    Analysis of the ensemble and polynomial chaos Kalman filters in Bayesian inverse problems.
    SIAM/ASA J. Uncertainty Quantification 3(1):823-851. [arXiv]
     
18. Ernst, O. G., Sprungk, B., Starkloff, H.-J. (2014)
    Bayesian inverse problems and Kalman filters.
    In: Dahlke S. et al. (Eds.) Extraction of Quantifiable Information from Complex Systems, Lecture Notes in Computational Science and Engineering, Band 102, Springer, Cham, pp. 133-159.
     
19. Ernst, O. G., Sprungk, B. (2014)
    Stochastic collocation for elliptic PDEs with random data - the lognormal case.
    In: J. Garcke und D. Pflüger (Eds.) Sparse Grids and Applications - Munich 2012, Lecture Notes in Computational Science and Engineering, Band 97, Springer, Cham, pp. 29-53.
     
20. Sprungk, B., van den Boogaart, K. G. (2013)
    Stochastic differential equations with fuzzy drift and diffusion.
    Fuzzy Sets and Systems 230(1):53-64.