Daniel Rudolf

Links

Coauthors

Research group

Former members of the research group

Past teaching

Research interests

Editorial work

Publications

(see also Articles on arXiv)

Preprints:

    • Wasserstein convergence rates of increasingly concentrating probability measures
    • with Mareike Hasenpflug and Björn Sprungk,
    • submitted. [arxiv]
    • Consistency of randomized integration methods
    • with Julian Hofstadler,
    • submitted. [arxiv]
    • Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors
    • with Björn Sprungk,
    • submitted. [arxiv]
    • Dimension-independent Markov chain Monte Carlo on the sphere,
    • with Han Cheng Lie, Björn Sprungk and Tim J. Sullivan,
    • submitted. [arxiv]
    • Perturbation theory for killed Markov processes and quasi-stationary distributions,
    • with Andi Q. Wang,
    • submitted. [arxiv]
    • The minimal spherical dispersion,
    • with Joscha Prochno,
    • submitted. [arxiv]
    • Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications,
    • with Laura Jula Vanegas, Benjamin Eltzner, Miroslav Dura, Stephan E. Lehnart, Axel Munk,
    • submitted. [arxiv]
    • Convergence of hybrid slice sampling via spectral gap,
    • with Krzysztof Latuszynski,
    • submitted. [arxiv]

Peer-reviewed publications:

    • The hit-and-run version of top-to-random,
    • with Samuel Boardman and Laurent Saloff-Coste,
    • J. Appl. Probab. 59, (2022), 860-879. [arxiv]
    • Geometric convergence of elliptical slice sampling,
    • with Viacheslav Natarovskii and Björn Sprungk,
    • Proceedings of the 38th ICML, PMLR 139, (2021), 7969-7978. [arxiv]
    • A strong law of large numbers for scrambled net integration,
    • with Art Owen,
    • SIAM Rev. 63, (2021), 360-372. [arxiv]
    • Stability of doubly-intractable distributions,
    • with Michael Habeck and Björn Sprungk,
    • Electron. Commun. Probab. 25, (2020), 1-13. [arxiv]
    • Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling,
    • with Viacheslav Natarovskii and Björn Sprungk,
    • Ann. Appl. Probab. 31, (2021), 806-825. [arxiv]
    • Expected dispersion of uniformly distributed points,
    • with Aicke Hinrichs, David Krieg and Robert J. Kunsch,
    • J. Complexity 61, (2020). [arxiv]
    • On a Metropolis-Hastings importance sampling estimator,
    • with Björn Sprungk,
    • Electron. J. Stat. 14, (2020), 857-889. [arxiv]
    • Optimal confidence for Monte Carlo integration of smooth functions,
    • with Robert J. Kunsch,
    • Adv. Comput. Math. 45, (2019), 3095-3122. [arxiv]
    • Perturbation bounds for Monte Carlo within Metropolis via restricted approximations,
    • with Felipe Medina-Aguayo and Nikolaus Schweizer,
    • Stoch. Proc. Appl. 130, (2020), 2200-2227. [arxiv]
    • The Amplitude-Phase Decomposition of the Magnetotelluric Impedance Tensor,
    • with Maik Neukirch, Xavier Garcia and Savitri Galiana,
    • Geophysics 84, (2019), A43-Z28. [arxiv]
    • A weighted discrepancy bound of quasi-Monte Carlo importance sampling,
    • with Josef Dick and Houying Zhu,
    • Stat. Prob. Letters 149 (2019), 100-106. [arxiv]
    • Solvable integration problems and optimal sample size selection,
    • with Robert J. Kunsch and Erich Novak,
    • J. Complexity 53 (2019), 40-67. [arxiv]
    • Maximum likelihood estimation in hidden Markov models with inhomogeneous noise,
    • with Manuel Diehn and Axel Munk,
    • ESAIM Probab. Stat. 23 (2019), 492-523. [arxiv]
    • Recovery algorithms for high-dimensional rank one tensors,
    • with David Krieg,
    • J. Approx. Theory 237 (2019), 17-29. [arxiv]
    • Comparison of hit-and-run, slice sampling and random walk Metropolis,
    • with Mario Ullrich,
    • J. Appl. Probab. 55 (2018), 1186-1202. [arxiv]
    • 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]
    • Perturbation theory for Markov chains via Wasserstein distance,
    • with Nikolaus Schweizer,
    • Bernoulli 24 (2018), 2610-2639. [arxiv]
    • On a generalization of the preconditioned Crank-Nicolson Metropolis algorithm,
    • with Björn Sprungk,
    • Found. Comput. Math. 18 (2018), 309-343. [arxiv]
    • Metropolis-Hastings Importance Sampling Estimator,
    • with Björn Sprungk,
    • PAMM Proc. Appl. Math. Mech. 17 (2017), 731-734. [pdf]
    • 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]
    • Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo,
    • with Josef Dick and Houying Zhu,
    • Ann. Appl. Probab. 26 (2016), 3178-3205. [arxiv]
    • Tractability of the approximation of high-dimensional rank one tensors,
    • with Erich Novak,
    • Constr. Approx. 43 (2016), 1-13. [arxiv]
    • Discussion of "Sequential Quasi-Monte-Carlo Sampling" by Gerber and Chopin,
    • J. R. Stat. Soc. Ser. B 77 (2015), 570-571. [pdf]
    • Error bounds of MCMC for functions with unbounded stationary variance,
    • with Nikolaus Schweizer,
    • Stat. Prob. Letters 99 (2015), 6-12. [arxiv]
    • Discrepancy estimates for variance bounding Markov chain quasi-Monte Carlo,
    • with Josef Dick,
    • Electron. J. Probab. 19 (2014), 1-24. [arxiv]
    • 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]
    • Positivity of hit-and-run and related algorithms,
    • with Mario Ullrich,
    • Electron. Commun. Probab. 18 (2013), 1-8. [arxiv]
    • Hit-and-run for numerical integration,
    • Monte Carlo and Quasi-Monte Carlo Methods 2012, Springer Proceedings in Mathematics & Statistics Volume 65 (2013), 597-612. [arxiv]
    • Explicit error bounds for Markov chain Monte Carlo,
    • Dissertationes Math. 485 (2012), 93 pp. [arxiv]
    • Error bounds for computing the expectation by Markov chain Monte Carlo,
    • Monte Carlo Meth. Appl. 16 (2010), 323-342. [arxiv]
    • Explicit error bounds for lazy reversible Markov chain Monte Carlo,
    • J. Complexity 25 (2009), 11-24. [arxiv]