Current courses (summer semester 2024)

• Methods in Machine Learning
• Stochastic Simulation
• Experimental design and multivariate statistics

Further courses

• Current Topics in Stochastics (winter, annually)
• Mathematics of Machine Learning (winter, annually)
• Methods in Machine Learning (summer, annual)
• Probabilistic Forecasting and Data Assimilation (summer, annual)
• Uncertainty Quantification (winter, odd-numbered years)
• Stochastic Methods for Material Science (winter, annual)
• Experimental Design and Multivariate Statistics (summer, annual)

• Uncertainty quantification for differential equations
• High-dimensional approximation methods
• Stochastic simulation methods, especially Markov chain Monte Carlo
• Bayesian inference for inverse problems
• Uncertainties in machine learning

Google Scholar Profile

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.
   
• D. Rudolf, B. Sprungk (2022)
  Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors.
  arXiv:2202.12127, 1-27.

Prefereed publications

1. M. Hasenpflug, D. Rudolf, B. Sprungk (2024)
   Wasserstein convergence rates of increasingly concentrating probability measures.
   Ann. Appl. Probab. 34(3):3320-3347. [arXiv]
    
2. 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). 
    
3. 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]
    
4. 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]
    
5. 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]
    
6. 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, vol. 144, Springer Cham, pp. 1-31. [arXiv]
    
7. 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]
    
8. Klebanov, I., Sprungk, B., Sullivan, T. J. (2021)
   The linear conditional expectation in Hilbert space.
   Bernoulli 27(4):2267-2299. [arXiv]
    
9. 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]
    
10. Habeck, M., Rudolf, D., Sprungk, B. (2020)
    Stability of doubly-intractable distributions.
    Electron. Commun. Probab. 25, paper no. 62, 13pp. [arXiv]
     
11. 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.
    Numerical Mathematics 145:915-971. [arXiv]
     
12. Rudolf, D., Sprungk, B. (2020)
    On a Metropolis-Hastings importance sampling estimator
    Electron. J. Statist. 14(1):857-889. [arXiv]
     
13. Sprungk, B. (2020)
    On the Local Lipschitz Robustness of Bayesian Inverse Problems.
    Inverse Problems 36:055015 (31pp). [arXiv]
     
14. 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]
     
15. Rudolf, D., Sprungk, B. (2018)
    On a Generalisation of the Preconditioned Crank-Nicolson Metropolis Algorithm.
    Found. Comput. Math. 18:309-343. [arXiv]
     
16. Rudolf, D., Sprungk, B. (2017)
    Metropolis-Hastings Importance Sampling Estimator.
    Proc. Appl. Math. Mech. 17:731-734.
     
17. 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.
     
18. 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]
     
19. 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, Vol. 102, Springer, Cham, pp. 133-159.
     
20. Ernst, O. G., Sprungk, B. (2014)
    Stochastic collocation for elliptic PDEs with random data - the lognormal case.
    In: J. Garcke and D. Pflüger (Eds.) Sparse Grids and Applications - Munich 2012, Lecture Notes in Computational Science and Engineering, Vol. 97, Springer, Cham, pp. 29-53.
     
21. Sprungk, B., van den Boogaart, K. G. (2013)
    Stochastic differential equations with fuzzy drift and diffusion.
    Fuzzy Sets and Systems 230(1):53-64.