@ 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
Germany

Links

Stud.IP
Felix-Bernstein-Institute for Mathematical Statistics
DFG Priority Program 1324
Research Training Group 2088
MATH Database (Zentralblatt)
MathSciNet
Coauthors:
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).

Teaching

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

Publications

(see also Articles on arXiv)

Preprints:

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