Institut für Mathematische Stochastik

Publikationen: Dr. Rudolf

  • Vanegas, L. J., Eltzner, B., Rudolf, D., Dura, M., Lehnart, S. E., Munk, A. (2021).
    Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications. arXiv:2103.06071. Submitted.
  • Natarovskii, V., Rudolf, D., Sprungk, B. (2019).
    Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling arXiv:1903.03824 . Submitted.
  • Diehn, M., Munk, A., Rudolf, D. (2019).
    Maximum likelihood estimation in hidden Markov models with inhomogeneous noise. arXiv:1804.04034 ESAIM: Probability and Statistics, 23, 492-523.
  • Kunsch, R. J., Novak, E., Rudolf, D. (2018).
    Solvable integration problems and optimal sample size selection Submitted.
  • Krieg, D., Rudolf, D. (2018).
    Recovery algorithms for high-dimensional rank one tensors J. Approx. Theory. Accepted.
  • Rudolf, D., Sprungk, B. (2018).
    Metropolis-Hastings importance sampling estimator. Proc. Appl. Math. Mech., 17, 731-734.
  • Rudolf, D., Sprungk, B. (2018).
    On a Metropolis-Hastings importance sampling estimator. arXiv:1805.07174 . Submitted.
  • Rudolf, D. (2018).
    An upper bound of the minimal dispersion via delta covers. Contemporary Computational Mathematics - a celebration of the 80th birthday of Ian Sloan, Springer-Verlag, 1099-1108.
  • Rudolf, D., Ullrich, M. (2018).
    Comparison of hit-and-run, slice sampling and random walk Metropolis, J. Appl. Probab.. Accepted.
  • Rudolf, D., Schweizer, N. (2018).
    Perturbation theory for Markov chains via Wasserstein distance. Bernoulli, 24, 2610-2639 Accepted.
  • Rudolf, D., Sprungk, B. (2018).
    On a generalization of the preconditioned Crank-Nicolson Metropolis algorithm. Found. Comput. Math. , 18, 309-343 Accepted.
  • Krieg, D., Rudolf, D. (2017).
    Recovery algorithms for high-dimensional rank one tensors Submitted.
  • Aistleitner, C., Hinrichs, A., Rudolf, D. (2017).
    On the size of the largest empty box amidst a point set. Discrete Appl. Math. , 230, 146-150.
  • Dick, J., Rudolf, D., Zhu, H. (2016).
    Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo. Ann. Appl. Probab., 26, 3178-3205.
  • Novak, E., Rudolf, D. (2016).
    Tractability of the approximation of high-dimensional rank one tensors. Constr. Approx., 43, 1-13.
  • Latuszynski, K., Rudolf, D. (2015).
    Convergence of hybrid slice sampling via spectral gap. Submitted.
  • Rudolf, D. (2015).
    Discussion of "Sequential Quasi-Monte-Carlo Sampling" by Gerber and Chopin J. R. Stat. Soc. Ser. B, 77, 570-571.
  • Rudolf, D., Schweizer, N. (2015).
    Error bounds of MCMC for functions with unbounded stationary variance, Stat. Prob. Letters, 99, 6-12.
  • Dick, J., Rudolf, D. (2014).
    Discrepancy estimates for variance bounding Markov chain quasi-Monte Carlo. Electron. J. Probab., 19, 1-24.
  • Novak, E., Rudolf, D. (2014).
    Computation of expectations by Markov chain Monte Carlo methods. Extraction of Quantifiable Information from Complex Systems, Lecture Notes in Computational Science and Engineering, 102, 397-411.
  • Rudolf, D., Ullrich, M. (2013).
    Positivity of hit-and-run and related algorithms. Electron. Commun. Probab., 18, 1-8.
  • Rudolf, D. (2013).
    Hit-and-run for numerical integration. Monte Carlo and Quasi-Monte Carlo Methods 2012, Springer Proceedings in Mathematics & Statistics, 65, 597-612.
  • Rudolf, D. (2012).
    Explicit error bounds for Markov chain Monte Carlo. Dissertationes Math., 485, 93 pp..
  • Rudolf, D. (2010).
    Error bounds for computing the expectation by Markov chain Monte Carlo. Monte Carlo Meth. Appl., 16, 323-342.
  • Rudolf, D. (2009).
    Explicit error bounds for lazy reversible Markov chain Monte Carlo. J. Complexity, 25, 11-24.