- 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.
|
