DR
Daniel Rudolf
math.PRcs.NAmath.NAmath.STstat.THstat.COstat.MEstat.MLcs.CGcs.LG
On Valency
published · living versionsW_a7axhd9v·v1 · currentpublished
Preprints & journals
49 papers in the corpus · 2008–2026Error bounds for simultaneous Wasserstein contractive adaptive increasingly rare MCMC2606.30018v1 · Julian Hofstadler, Daniel Rudolf2026 · 0 citationsarXiv
Delayed Acceptance Slice Sampling2512.17868v2 · Kevin Bitterlich, Daniel Rudolf, Bjorn Sprungk2025 · 0 citationsarXiv
Perturbations of Markov Chains2404.10251v1 · Daniel Rudolf, Aaron Smith, Matias Quiroz2024 · 0 citationsarXiv
Weak Poincaré inequality comparisons for ideal and hybrid slice sampling2402.13678v2 · Sam Power, Daniel Rudolf, Bjorn Sprungk et al.2024 · 1 citationarXiv
Almost sure convergence rates of adaptive increasingly rare Markov chain Monte Carlo2402.12122v2 · Julian Hofstadler, Krzysztof Latuszynski, Gareth O. Roberts et al.2024 · 3 citationsarXiv
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo2401.16567v2 · Philip Schar, Michael Habeck, Daniel Rudolf2024 · 0 citationsProceedings of the 41st International Conference on Machine Learning, PMLR 235:43571-43607, 2024
The Minimal Spherical Dispersion.38261923 · Prochno, Joscha, Rudolf, Daniel2025 · 0 citationsJournal of geometric analysis. 2024;34(3):85
Geodesic slice sampling on Riemannian manifolds2312.00417v3 · Alain Durmus, Samuel Gruffaz, Mareike Hasenpflug et al.2023 · 1 citationarXiv
Bayesian Maximum Entropy Ensemble Refinement10.1101/2023.09.12.557310v1 · Eltzner, B., Hofstadler, J., Rudolf, D. et al.2023 · 0 citationsbioRxiv
Dimension-independent spectral gap of polar slice sampling2305.03685v2 · Daniel Rudolf, Philip Schar2023 · 3 citationsStatistics and Computing 34, article 20, 2024
Gibbsian polar slice sampling2302.03945v2 · Philip Schar, Michael Habeck, Daniel Rudolf2023 · 2 citationsProceedings of the 40th International Conference on Machine Learning, PMLR 202:30204-30223, 2023
Geodesic slice sampling on the sphere2301.08056v5 · Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar et al.2023 · 0 citationsarXiv
Reversibility of elliptical slice sampling revisited2301.02426v2 · Mareike Hasenpflug, Viacheslav Telezhnikov, Daniel Rudolf2023 · 2 citationsarXiv
Wasserstein convergence rates of increasingly concentrating probability measures2207.08551v2 · Mareike Hasenpflug, Daniel Rudolf, Bjorn Sprungk2022 · 3 citationsarXiv
Consistency of randomized integration methods2203.17010v3 · Julian Hofstadler, Daniel Rudolf2022 · 4 citationsarXiv
Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors2202.12127v1 · Daniel Rudolf, Bjorn Sprungk2022 · 1 citationarXiv
Dimension-independent Markov chain Monte Carlo on the sphere2112.12185v4 · H. C. Lie, D. Rudolf, B. Sprungk et al.2021 · 5 citationsScandinavian Journal of Statistics (2023)
Perturbation theory for killed Markov processes and quasi-stationary distributions2109.13819v2 · Daniel Rudolf, Andi Q. Wang2021 · 1 citationAdv. Appl. Probab. 57 (2025) 1138-1165
Geometric convergence of elliptical slice sampling2105.03308v3 · Viacheslav Natarovskii, Daniel Rudolf, Bjorn Sprungk2021 · 5 citationsarXiv
Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications to ion channels2103.06071v3 · Laura Jula Vanegas, Benjamin Eltzner, Daniel Rudolf et al.2021 · 1 citationarXiv
The hit-and-run version of top-to-random2009.04977v2 · Samuel Boardman, Daniel Rudolf, Laurent Saloff-Coste2020 · 3 citationsarXiv
Stability of doubly-intractable distributions2004.07310v3 · Michael Habeck, Daniel Rudolf, Bjorn Sprungk2020 · 3 citationsarXiv
Perturbation bounds for Monte Carlo within Metropolis via restricted approximations.32255890 · Medina-Aguayo, Felipe, Rudolf, Daniel, Schweizer, Nikolaus2020 · 25 citationsStochastic processes and their applications. 2020;130(4):2200-2227
A strong law of large numbers for scrambled net integration2002.07859v3 · Art B. Owen, Daniel Rudolf2020 · 18 citationsarXiv
Expected dispersion of uniformly distributed points1911.12074v2 · Aicke Hinrichs, David Krieg, Robert J. Kunsch et al.2019 · 13 citationsarXiv
Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling1903.03824v2 · Viacheslav Natarovskii, Daniel Rudolf, Bjorn Sprungk2019 · 11 citationsarXiv
A weighted Discrepancy Bound of quasi-Monte Carlo Importance Sampling1901.08115v1 · Josef Dick, Daniel Rudolf, Houying Zhu2019 · 8 citationsarXiv
Optimal confidence for Monte Carlo integration of smooth functions1809.09890v1 · Robert J. Kunsch, Daniel Rudolf2018 · 14 citationsarXiv
Solvable Integration Problems and Optimal Sample Size Selection1805.08637v2 · Robert J. Kunsch, Erich Novak, Daniel Rudolf2018 · 17 citationsarXiv
On a Metropolis-Hastings importance sampling estimator1805.07174v3 · Daniel Rudolf, Bjorn Sprungk2018 · 12 citationsElectron. J. Statist. 14(1) (2020) 857-889
Maximum likelihood estimation in hidden Markov models with inhomogeneous noise1804.04034v2 · Manuel Diehn, Axel Munk, Daniel Rudolf2018 · 9 citationsarXiv
Recovery algorithms for high-dimensional rank one tensors1711.03986v2 · David Krieg, Daniel Rudolf2017 · 9 citationsarXiv
The Amplitude-Phase Decomposition for the Magnetotelluric Impedance Tensor1704.02943v2 · Maik Neukirch, Daniel Rudolf, Xavier Garcia et al.2017 · 8 citationsarXiv
An Upper Bound of the Minimal Dispersion via Delta Covers1701.06430v3 · Daniel Rudolf2017 · 29 citationsarXiv
On the size of the largest empty box amidst a point set1507.02067v3 · Christoph Aistleitner, Aicke Hinrichs, Daniel Rudolf2015 · 40 citationsarXiv
Comparison of hit-and-run, slice sampling and random walk Metropolis1505.00579v3 · Daniel Rudolf, Mario Ullrich2015 · 10 citationsarXiv
On a generalization of the preconditioned Crank-Nicolson Metropolis algorithm1504.03461v2 · Daniel Rudolf, Bjorn Sprungk2015 · 70 citationsarXiv
Perturbation theory for Markov chains via Wasserstein distance1503.04123v3 · Daniel Rudolf, Nikolaus Schweizer2015 · 96 citationsarXiv
Convergence of hybrid slice sampling via spectral gap1409.2709v2 · Krzysztof \Latuszy'nski, Daniel Rudolf2014 · 12 citationsAdv. Appl. Probab. 56 (2024) 1440-1466
Tractability of the approximation of high-dimensional rank one tensors1402.5011v2 · Erich Novak, Daniel Rudolf2014 · 19 citationsarXiv
Error bounds of MCMC for functions with unbounded stationary variance1312.4344v2 · Daniel Rudolf, Nikolaus Schweizer2013 · 10 citationsStatistics & Probability Letters, Volume 99, April 2015, Pages 6-12
Discrepancy estimates for variance bounding Markov chain quasi-Monte Carlo1311.1890v2 · Josef Dick, Daniel Rudolf2013 · 10 citationsElectron. J. Probab. 19 (2014), no. 105, 1-24
Computation of expectations by Markov chain Monte Carlo methods1311.1899v2 · Erich Novak, Daniel Rudolf2013 · 9 citationsExtraction of Quantifiable Information from Complex Systems, Lecture Notes in Computational Science and Engineering, Volume 102, 2014, pp 397-411
Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo1303.2423v3 · Josef Dick, Daniel Rudolf, Houying Zhu2013 · 16 citationsarXiv
Hit-and-run for numerical integration1212.4486v1 · Daniel Rudolf2012 · 16 citationsMonte Carlo and Quasi-Monte Carlo Methods 2012, Springer Proceedings in Mathematics & Statistics Volume 65, 2013, pp 597-612
Positivity of hit-and-run and related algorithms1212.4512v2 · Daniel Rudolf, Mario Ullrich2012 · 26 citationsElectron. Commun. Probab. 18 (2013), no. 49, 1-8
Explicit error bounds for Markov chain Monte Carlo1108.3201v2 · Daniel Rudolf2011 · 75 citationsDissertationes Math. 485 (2012), 93 pp
Error bounds for computing the expectation by Markov chain Monte Carlo0906.2359v2 · Daniel Rudolf2009 · 5 citationsMonte Carlo Methods and Applications. Volume 16, Issue 3-4, Pages 323-342, 2010
Explicit error bounds for lazy reversible Markov Chain Monte Carlo0805.3587v2 · Daniel Rudolf2008 · 34 citationsD. Rudolf, Explicit error bounds for lazy reversible Markov chain Monte Carlo, J. Complexity, 25(1):11--24, 2009
Career total: 153 works. 49 are in this corpus.
Profile built from the corpus for this byline.
Author records are still filling in while the Hub is in alpha. If this is your page, you'll be able to claim it soon. Spot a mistake? Tell us.