@ IMS Göttingen Mathematics at the Univ. Göttingen Georg-August-Universität Göttingen

Daniel Rudolf

Junior Professor at the Institute for Mathematical Stochastics of the Georg-August-Universität Göttingen.
Institution: Georg-August-Universität Göttingen
Institute for Mathematical Stochastics
Email: daniel.rudolf (at) uni-goettingen.de
Phone: +49 551 39 26100
Fax: +49 551 39 13505
Office: Raum 2.185B, Gebäude GZG
Address: Institut für Mathematische Stochastik
Georg-August-Universität Göttingen
Goldschmidtstr. 3-5
37077 Göttingen


Felix-Bernstein-Institute for Mathematical Statistics
DFG Priority Program 1324
Research Training Group 2088
MATH Database (Zentralblatt)
Christoph Aistleitner Josef Dick Manuel Diehn Aicke Hinrichs David Krieg Robert J. Kunsch Krzysztof Latuszynski Axel Munk Erich Novak Nikolaus Schweizer Björn Sprungk Mario Ullrich Houying Zhu.
Research group:
Viacheslav Natarovskii (PhD student),  Björn Sprungk (Postdoc).


Stochastik (2018)
Maß- und Wahrscheinlichkeitstheorie (2017/18)
Markovketten Monte-Carlo Methoden (2015) [pdf]
Versicherungsmathematik (2014/15)
Markov chain Monte Carlo on general state spaces (2012) [pdf]

Research interests


(see also Articles on arXiv)


4. Solvable integration problems and optimal sample size selection,
with Robert J. Kunsch and Erich Novak,
submitted. [arxiv]
3. On a Metropolis-Hastings importance sampling estimator,
with Björn Sprungk,
submitted. [arxiv]
2. Maximum likelihood estimation in hidden Markov models with inhomogeneous noise,
with Manuel Diehn and Axel Munk,
submitted. [arxiv]
1. Convergence of hybrid slice sampling via spectral gap,
with Krzysztof Latuszynski,
submitted. [arxiv]

Peer-reviewed publications:

18. Recovery algorithms for high-dimensional rank one tensors,
with David Krieg,
Accepted for publication J. Approx. Theory [arxiv]
17. Comparison of hit-and-run, slice sampling and random walk Metropolis,
with Mario Ullrich,
Accepted for publication J. Appl. Probab. [arxiv]
16. An upper bound of the minimal dispersion via delta covers,
Contemporary Computational Mathematics - A Celebration of the 80th Birthday of Ian Sloan, Springer-Verlag, (2018), 1099-1108. [arxiv]
15. Perturbation theory for Markov chains via Wasserstein distance,
with Nikolaus Schweizer,
Bernoulli 24 (2018), 2610-2639. [arxiv]
14. On a generalization of the preconditioned Crank-Nicolson Metropolis algorithm,
with Björn Sprungk,
Found. Comput. Math. 18 (2018), 309-343. [arxiv]
13. Metropolis-Hastings Importance Sampling Estimator,
with Björn Sprungk,
PAMM Proc. Appl. Math. Mech. 17 (2017), 731-734. [pdf]
12. On the size of the largest empty box amidst a point set,
with Christoph Aistleitner and Aicke Hinrichs,
Discrete Appl. Math. 230 (2017), 146-150. [arxiv]
11. Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo,
with Josef Dick and Houying Zhu,
Ann. Appl. Probab. 26 (2016), 3178-3205. [arxiv]
10. Tractability of the approximation of high-dimensional rank one tensors,
with Erich Novak,
Constr. Approx. 43 (2016), 1-13. [arxiv]
9. Discussion of "Sequential Quasi-Monte-Carlo Sampling" by Gerber and Chopin,
J. R. Stat. Soc. Ser. B 77 (2015), 570-571. [pdf]
8. Error bounds of MCMC for functions with unbounded stationary variance,
with Nikolaus Schweizer,
Stat. Prob. Letters 99 (2015), 6-12. [arxiv]
7. Discrepancy estimates for variance bounding Markov chain quasi-Monte Carlo,
with Josef Dick,
Electron. J. Probab. 19 (2014), 1-24. [arxiv]
6. Computation of expectations by Markov chain Monte Carlo methods,
with Erich Novak,
Extraction of Quantifiable Information from Complex Systems, Lecture Notes in Computational Science and Engineering Volume 102 (2014), 397-411. [arxiv]
5. Positivity of hit-and-run and related algorithms,
with Mario Ullrich,
Electron. Commun. Probab. 18 (2013), 1-8. [pdf | arxiv]
4. Hit-and-run for numerical integration,
Monte Carlo and Quasi-Monte Carlo Methods 2012, Springer Proceedings in Mathematics & Statistics Volume 65 (2013), 597-612. [pdf | arxiv]
3. Explicit error bounds for Markov chain Monte Carlo,
Dissertationes Math. 485 (2012), 93 pp. [pdf | arxiv]
2. Error bounds for computing the expectation by Markov chain Monte Carlo,
Monte Carlo Meth. Appl. 16 (2010), 323-342. [pdf | arxiv]
1. Explicit error bounds for lazy reversible Markov chain Monte Carlo,
J. Complexity 25 (2009), 11-24. [pdf | arxiv]
@ IMS Göttingen Mathematics at the Univ. Göttingen Georg-August-Universität Göttingen