Retrieve arXiv Information

Format:

Preprints:

Please add the journal reference and DOI for your papers as soon as they are published.

@misc{2407.15455,
Author = {Elizabeth L. Baker and Moritz Schauer and Stefan Sommer},
Title = {Score matching for bridges without time-reversals},
Year = {2024},
Eprint = {arXiv:2407.15455},
}

@misc{2406.14451,
Author = {Gaurav Arya and Moritz Schauer and Ruben Seyer},
Title = {Gradient Estimation via Differentiable Metropolis-Hastings},
Year = {2024},
Eprint = {arXiv:2406.14451},
}

@misc{2403.05409,
Author = {Marc Corstanje and Frank van der Meulen and Moritz Schauer and Stefan Sommer},
Title = {Simulating conditioned diffusions on manifolds},
Year = {2024},
Eprint = {arXiv:2403.05409},
}

@misc{2306.07961,
Author = {Gaurav Arya and Ruben Seyer and Frank Schäfer and Kartik Chandra and Alexander K. Lew and Mathieu Huot and Vikash K. Mansinghka and Jonathan Ragan-Kelley and Christopher Rackauckas and Moritz Schauer},
Title = {Differentiating Metropolis-Hastings to Optimize Intractable Densities},
Year = {2023},
Eprint = {arXiv:2306.07961},
}

@misc{2303.08023,
Author = {Joris Bierkens and Sebastiano Grazzi and Gareth Roberts and Moritz Schauer},
Title = {Methods and applications of PDMP samplers with boundary conditions},
Year = {2023},
Eprint = {arXiv:2303.08023},
}

@misc{2210.08572,
Author = {Gaurav Arya and Moritz Schauer and Frank Schäfer and Chris Rackauckas},
Title = {Automatic Differentiation of Programs with Discrete Randomness},
Year = {2022},
Eprint = {arXiv:2210.08572},
}

@misc{2206.03256,
Author = {Raphael Sonabend and Florian Pfisterer and Alan Mishler and Moritz Schauer and Lukas Burk and Sumantrak Mukherjee and Sebastian Vollmer},
Title = {Flexible Group Fairness Metrics for Survival Analysis},
Year = {2022},
Eprint = {arXiv:2206.03256},
}

@misc{2010.03509,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Automatic Backward Filtering Forward Guiding for Markov processes and graphical models},
Year = {2020},
Eprint = {arXiv:2010.03509},
}

@misc{1804.10222,
Author = {Richard C. Kraaij and Moritz Schauer},
Title = {A generator approach to stochastic monotonicity and propagation of order},
Year = {2018},
Eprint = {arXiv:1804.10222},
}

@misc{1708.04870,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {On residual and guided proposals for diffusion bridge simulation},
Year = {2017},
Eprint = {arXiv:1708.04870},
}

Published:

These BibTeX records are based on arXiv information only. You may prefer getting the more detailed records provided by MathSciNet instead.

@article{2310.05655,
Author = {Moritz Schauer and Marcel Wienöbst},
Title = {Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs},
Year = {2023},
Eprint = {arXiv:2310.05655},
Howpublished = {Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024},
}

@article{2111.11377,
Author = {Marc Corstanje and Frank van der Meulen and Moritz Schauer},
Title = {Conditioning continuous-time Markov processes by guiding},
Year = {2021},
Eprint = {arXiv:2111.11377},
Howpublished = {Stochastics 95(6), 2022, pp. 963-996},
Doi = {10.1080/17442508.2022.215008},
}

@article{2110.00602,
Author = {Chad Scherrer and Moritz Schauer},
Title = {Applied Measure Theory for Probabilistic Modeling},
Year = {2021},
Eprint = {arXiv:2110.00602},
Howpublished = {JuliaCon Proceedings, 1(1), 92 (2022)},
Doi = {10.21105/jcon.00092},
}

@article{2108.11891,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Weak solutions to gamma-driven stochastic differential equations},
Year = {2021},
Eprint = {arXiv:2108.11891},
Howpublished = {Indagationes Mathematicae 34(4), pp. 820-829 (July 2023)},
Doi = {10.1016/j.indag.2023.03.004},
}

@article{2103.08478,
Author = {Joris Bierkens and Sebastiano Grazzi and Frank van der Meulen and Moritz Schauer},
Title = {Sticky PDMP samplers for sparse and local inference problems},
Year = {2021},
Eprint = {arXiv:2103.08478},
Howpublished = {Stat Comput 33, 8 (2023)},
Doi = {10.1007/s11222-022-10180-5},
}

@article{2011.08321,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations},
Year = {2020},
Eprint = {arXiv:2011.08321},
Howpublished = {Bernoulli 28(4), 2022, pp. 2151-2180},
Doi = {10.3150/21-BEJ1413},
}

@article{2002.00885,
Author = {Alexis Arnaudon and Frank van der Meulen and Moritz Schauer and Stefan Sommer},
Title = {Diffusion bridges for stochastic Hamiltonian systems and shape evolutions},
Year = {2020},
Eprint = {arXiv:2002.00885},
Howpublished = {SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323},
Doi = {10.1137/21M1406283},
}

@article{2001.05889,
Author = {Joris Bierkens and Sebastiano Grazzi and Frank van der Meulen and Moritz Schauer},
Title = {A piecewise deterministic Monte Carlo method for diffusion bridges},
Year = {2020},
Eprint = {arXiv:2001.05889},
Howpublished = {Stat Comput 31, 37 (2021)},
Doi = {10.1007/s11222-021-10008-8},
}

@article{1810.07668,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Bayesian wavelet de-noising with the caravan prior},
Year = {2018},
Eprint = {arXiv:1810.07668},
Howpublished = {ESAIM Probab. Stat., Volume 23, pages 947-978, 2019},
Doi = {10.1051/ps/2019019},
}

@article{1810.01761,
Author = {Joris Bierkens and Frank van der Meulen and Moritz Schauer},
Title = {Simulation of elliptic and hypo-elliptic conditional diffusions},
Year = {2018},
Eprint = {arXiv:1810.01761},
Howpublished = {Adv. Appl. Probab. 52 (2020) 173-212},
Doi = {10.1017/apr.2019.54},
}

@article{1805.05606,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility learning under microstructure noise},
Year = {2018},
Eprint = {arXiv:1805.05606},
Howpublished = {Jpn. J. Stat. Data. Sci 6, 551-571 (2023)},
Doi = {10.1007/s42081-022-00185-9},
}

@article{1804.11267,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian inference for Gamma-type L\'evy subordinators},
Year = {2018},
Eprint = {arXiv:1804.11267},
Howpublished = {Communications in Mathematical Sciences, Volume 17, Number 3, 2019},
Doi = {10.4310/CMS.2019.v17.n3.a8},
}

@article{1804.03616,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Fast and scalable non-parametric Bayesian inference for Poisson point processes},
Year = {2018},
Eprint = {arXiv:1804.03616},
Howpublished = {RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6},
}

@article{1801.09956,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility estimation},
Year = {2018},
Eprint = {arXiv:1801.09956},
Howpublished = {2017 MATRIX Annals, Springer International Publishing, 2019},
Doi = {10.1007/978-3-030-04161-8_19},
}

@article{1712.03807,
Author = {Marcin Mider and Moritz Schauer and Frank van der Meulen},
Title = {Continuous-discrete smoothing of diffusions},
Year = {2017},
Eprint = {arXiv:1712.03807},
Howpublished = {Electron. J. Statist. 15(2): 4295-4342 (2021)},
Doi = {10.1214/21-EJS1894},
}

@article{1706.07449,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient},
Year = {2017},
Eprint = {arXiv:1706.07449},
Howpublished = {Braz. J. Probab. Stat., 34(3): 537-579. 2020},
Doi = {10.1214/19-BJPS433},
}

@article{1612.05124,
Author = {Frank van der Meulen and Moritz Schauer and Jan van Waaij},
Title = {Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions},
Year = {2016},
Eprint = {arXiv:1612.05124},
Howpublished = {J. Stat Inference Stoch Process (2018) 21: 603},
Doi = {10.1007/s11203-017-9163-7},
}

@article{1606.04082,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Bayesian estimation of incompletely observed diffusions},
Year = {2016},
Eprint = {arXiv:1606.04082},
Howpublished = {Stochastics 90 (5), 2018, pp. 641-662},
Doi = {10.1080/17442508.2017.1381097},
}

@article{1406.4704,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals},
Year = {2014},
Eprint = {arXiv:1406.4704},
Howpublished = {Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396},
Doi = {10.1214/17-EJS1290},
}

@article{1311.3606,
Author = {Moritz Schauer and Frank van der Meulen and Harry van Zanten},
Title = {Guided proposals for simulating multi-dimensional diffusion bridges},
Year = {2013},
Eprint = {arXiv:1311.3606},
Howpublished = {Bernoulli Volume 23, Number 4A (November 2017), 2917-2950},
Doi = {10.3150/16-BEJ833},
}

@article{1206.4910,
Author = {Frank van der Meulen and Moritz Schauer and Harry van Zanten},
Title = {Reversible jump MCMC for nonparametric drift estimation for diffusion processes},
Year = {2012},
Eprint = {arXiv:1206.4910},
Howpublished = {Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014)},
Doi = {10.1016/j.csda.2013.03.002},
}

Preprints:

Please add the journal reference and DOI for your papers as soon as they are published.

@online{2407.15455,
Author = {Elizabeth L. Baker and Moritz Schauer and Stefan Sommer},
Title = {Score matching for bridges without time-reversals},
Year = {2024},
Eprint = {2407.15455},
Eprinttype = {arXiv},
}

@online{2406.14451,
Author = {Gaurav Arya and Moritz Schauer and Ruben Seyer},
Title = {Gradient Estimation via Differentiable Metropolis-Hastings},
Year = {2024},
Eprint = {2406.14451},
Eprinttype = {arXiv},
}

@online{2403.05409,
Author = {Marc Corstanje and Frank van der Meulen and Moritz Schauer and Stefan Sommer},
Title = {Simulating conditioned diffusions on manifolds},
Year = {2024},
Eprint = {2403.05409},
Eprinttype = {arXiv},
}

@online{2306.07961,
Author = {Gaurav Arya and Ruben Seyer and Frank Schäfer and Kartik Chandra and Alexander K. Lew and Mathieu Huot and Vikash K. Mansinghka and Jonathan Ragan-Kelley and Christopher Rackauckas and Moritz Schauer},
Title = {Differentiating Metropolis-Hastings to Optimize Intractable Densities},
Year = {2023},
Eprint = {2306.07961},
Eprinttype = {arXiv},
}

@online{2303.08023,
Author = {Joris Bierkens and Sebastiano Grazzi and Gareth Roberts and Moritz Schauer},
Title = {Methods and applications of PDMP samplers with boundary conditions},
Year = {2023},
Eprint = {2303.08023},
Eprinttype = {arXiv},
}

@online{2210.08572,
Author = {Gaurav Arya and Moritz Schauer and Frank Schäfer and Chris Rackauckas},
Title = {Automatic Differentiation of Programs with Discrete Randomness},
Year = {2022},
Eprint = {2210.08572},
Eprinttype = {arXiv},
}

@online{2206.03256,
Author = {Raphael Sonabend and Florian Pfisterer and Alan Mishler and Moritz Schauer and Lukas Burk and Sumantrak Mukherjee and Sebastian Vollmer},
Title = {Flexible Group Fairness Metrics for Survival Analysis},
Year = {2022},
Eprint = {2206.03256},
Eprinttype = {arXiv},
}

@online{2010.03509,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Automatic Backward Filtering Forward Guiding for Markov processes and graphical models},
Year = {2020},
Eprint = {2010.03509},
Eprinttype = {arXiv},
}

@online{1804.10222,
Author = {Richard C. Kraaij and Moritz Schauer},
Title = {A generator approach to stochastic monotonicity and propagation of order},
Year = {2018},
Eprint = {1804.10222},
Eprinttype = {arXiv},
}

@online{1708.04870,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {On residual and guided proposals for diffusion bridge simulation},
Year = {2017},
Eprint = {1708.04870},
Eprinttype = {arXiv},
}

Published:

These BibLaTeX records are based on arXiv information only. You may prefer getting the more detailed records provided by MathSciNet instead.

@article{2310.05655,
Author = {Moritz Schauer and Marcel Wienöbst},
Title = {Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs},
Year = {2023},
Eprint = {2310.05655},
Eprinttype = {arXiv},
Howpublished = {Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024},
}

@article{2111.11377,
Author = {Marc Corstanje and Frank van der Meulen and Moritz Schauer},
Title = {Conditioning continuous-time Markov processes by guiding},
Year = {2021},
Eprint = {2111.11377},
Eprinttype = {arXiv},
Howpublished = {Stochastics 95(6), 2022, pp. 963-996},
Doi = {10.1080/17442508.2022.215008},
}

@article{2110.00602,
Author = {Chad Scherrer and Moritz Schauer},
Title = {Applied Measure Theory for Probabilistic Modeling},
Year = {2021},
Eprint = {2110.00602},
Eprinttype = {arXiv},
Howpublished = {JuliaCon Proceedings, 1(1), 92 (2022)},
Doi = {10.21105/jcon.00092},
}

@article{2108.11891,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Weak solutions to gamma-driven stochastic differential equations},
Year = {2021},
Eprint = {2108.11891},
Eprinttype = {arXiv},
Howpublished = {Indagationes Mathematicae 34(4), pp. 820-829 (July 2023)},
Doi = {10.1016/j.indag.2023.03.004},
}

@article{2103.08478,
Author = {Joris Bierkens and Sebastiano Grazzi and Frank van der Meulen and Moritz Schauer},
Title = {Sticky PDMP samplers for sparse and local inference problems},
Year = {2021},
Eprint = {2103.08478},
Eprinttype = {arXiv},
Howpublished = {Stat Comput 33, 8 (2023)},
Doi = {10.1007/s11222-022-10180-5},
}

@article{2011.08321,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations},
Year = {2020},
Eprint = {2011.08321},
Eprinttype = {arXiv},
Howpublished = {Bernoulli 28(4), 2022, pp. 2151-2180},
Doi = {10.3150/21-BEJ1413},
}

@article{2002.00885,
Author = {Alexis Arnaudon and Frank van der Meulen and Moritz Schauer and Stefan Sommer},
Title = {Diffusion bridges for stochastic Hamiltonian systems and shape evolutions},
Year = {2020},
Eprint = {2002.00885},
Eprinttype = {arXiv},
Howpublished = {SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323},
Doi = {10.1137/21M1406283},
}

@article{2001.05889,
Author = {Joris Bierkens and Sebastiano Grazzi and Frank van der Meulen and Moritz Schauer},
Title = {A piecewise deterministic Monte Carlo method for diffusion bridges},
Year = {2020},
Eprint = {2001.05889},
Eprinttype = {arXiv},
Howpublished = {Stat Comput 31, 37 (2021)},
Doi = {10.1007/s11222-021-10008-8},
}

@article{1810.07668,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Bayesian wavelet de-noising with the caravan prior},
Year = {2018},
Eprint = {1810.07668},
Eprinttype = {arXiv},
Howpublished = {ESAIM Probab. Stat., Volume 23, pages 947-978, 2019},
Doi = {10.1051/ps/2019019},
}

@article{1810.01761,
Author = {Joris Bierkens and Frank van der Meulen and Moritz Schauer},
Title = {Simulation of elliptic and hypo-elliptic conditional diffusions},
Year = {2018},
Eprint = {1810.01761},
Eprinttype = {arXiv},
Howpublished = {Adv. Appl. Probab. 52 (2020) 173-212},
Doi = {10.1017/apr.2019.54},
}

@article{1805.05606,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility learning under microstructure noise},
Year = {2018},
Eprint = {1805.05606},
Eprinttype = {arXiv},
Howpublished = {Jpn. J. Stat. Data. Sci 6, 551-571 (2023)},
Doi = {10.1007/s42081-022-00185-9},
}

@article{1804.11267,
Author = {Denis Belomestny and Shota Gugushvili and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian inference for Gamma-type L\'evy subordinators},
Year = {2018},
Eprint = {1804.11267},
Eprinttype = {arXiv},
Howpublished = {Communications in Mathematical Sciences, Volume 17, Number 3, 2019},
Doi = {10.4310/CMS.2019.v17.n3.a8},
}

@article{1804.03616,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Fast and scalable non-parametric Bayesian inference for Poisson point processes},
Year = {2018},
Eprint = {1804.03616},
Eprinttype = {arXiv},
Howpublished = {RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6},
}

@article{1801.09956,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian volatility estimation},
Year = {2018},
Eprint = {1801.09956},
Eprinttype = {arXiv},
Howpublished = {2017 MATRIX Annals, Springer International Publishing, 2019},
Doi = {10.1007/978-3-030-04161-8_19},
}

@article{1712.03807,
Author = {Marcin Mider and Moritz Schauer and Frank van der Meulen},
Title = {Continuous-discrete smoothing of diffusions},
Year = {2017},
Eprint = {1712.03807},
Eprinttype = {arXiv},
Howpublished = {Electron. J. Statist. 15(2): 4295-4342 (2021)},
Doi = {10.1214/21-EJS1894},
}

@article{1706.07449,
Author = {Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
Title = {Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient},
Year = {2017},
Eprint = {1706.07449},
Eprinttype = {arXiv},
Howpublished = {Braz. J. Probab. Stat., 34(3): 537-579. 2020},
Doi = {10.1214/19-BJPS433},
}

@article{1612.05124,
Author = {Frank van der Meulen and Moritz Schauer and Jan van Waaij},
Title = {Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions},
Year = {2016},
Eprint = {1612.05124},
Eprinttype = {arXiv},
Howpublished = {J. Stat Inference Stoch Process (2018) 21: 603},
Doi = {10.1007/s11203-017-9163-7},
}

@article{1606.04082,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Bayesian estimation of incompletely observed diffusions},
Year = {2016},
Eprint = {1606.04082},
Eprinttype = {arXiv},
Howpublished = {Stochastics 90 (5), 2018, pp. 641-662},
Doi = {10.1080/17442508.2017.1381097},
}

@article{1406.4704,
Author = {Frank van der Meulen and Moritz Schauer},
Title = {Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals},
Year = {2014},
Eprint = {1406.4704},
Eprinttype = {arXiv},
Howpublished = {Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396},
Doi = {10.1214/17-EJS1290},
}

@article{1311.3606,
Author = {Moritz Schauer and Frank van der Meulen and Harry van Zanten},
Title = {Guided proposals for simulating multi-dimensional diffusion bridges},
Year = {2013},
Eprint = {1311.3606},
Eprinttype = {arXiv},
Howpublished = {Bernoulli Volume 23, Number 4A (November 2017), 2917-2950},
Doi = {10.3150/16-BEJ833},
}

@article{1206.4910,
Author = {Frank van der Meulen and Moritz Schauer and Harry van Zanten},
Title = {Reversible jump MCMC for nonparametric drift estimation for diffusion processes},
Year = {2012},
Eprint = {1206.4910},
Eprinttype = {arXiv},
Howpublished = {Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014)},
Doi = {10.1016/j.csda.2013.03.002},
}

Preprints:

Please add the journal reference and DOI for your papers as soon as they are published.

Simple-minded \bibitems:

\begin{thebibliography}

\bibitem{2407.15455}
Elizabeth L. Baker, Moritz Schauer and Stefan Sommer.
\newblock Score matching for bridges without time-reversals, 2024;
\newblock arXiv:2407.15455.

\bibitem{2406.14451}
Gaurav Arya, Moritz Schauer and Ruben Seyer.
\newblock Gradient Estimation via Differentiable Metropolis-Hastings, 2024;
\newblock arXiv:2406.14451.

\bibitem{2403.05409}
Marc Corstanje, Frank van der Meulen, Moritz Schauer and Stefan Sommer.
\newblock Simulating conditioned diffusions on manifolds, 2024;
\newblock arXiv:2403.05409.

\bibitem{2306.07961}
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas and Moritz Schauer.
\newblock Differentiating Metropolis-Hastings to Optimize Intractable Densities, 2023;
\newblock arXiv:2306.07961.

\bibitem{2303.08023}
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts and Moritz Schauer.
\newblock Methods and applications of PDMP samplers with boundary conditions, 2023;
\newblock arXiv:2303.08023.

\bibitem{2210.08572}
Gaurav Arya, Moritz Schauer, Frank Schäfer and Chris Rackauckas.
\newblock Automatic Differentiation of Programs with Discrete Randomness, 2022;
\newblock arXiv:2210.08572.

\bibitem{2206.03256}
Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee and Sebastian Vollmer.
\newblock Flexible Group Fairness Metrics for Survival Analysis, 2022;
\newblock arXiv:2206.03256.

\bibitem{2010.03509}
Frank van der Meulen and Moritz Schauer.
\newblock Automatic Backward Filtering Forward Guiding for Markov processes and graphical models, 2020;
\newblock arXiv:2010.03509.

\bibitem{1804.10222}
Richard C. Kraaij and Moritz Schauer.
\newblock A generator approach to stochastic monotonicity and propagation of order, 2018;
\newblock arXiv:1804.10222.

\bibitem{1708.04870}
Frank van der Meulen and Moritz Schauer.
\newblock On residual and guided proposals for diffusion bridge simulation, 2017;
\newblock arXiv:1708.04870.

\end{thebibliography}

Published:

Simple-minded \bibitems:

\begin{thebibliography}

\bibitem{2310.05655}
Moritz Schauer and Marcel Wienöbst.
\newblock Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs, 2023,
\newblock Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024;
\newblock arXiv:2310.05655.

\bibitem{2111.11377}
Marc Corstanje, Frank van der Meulen and Moritz Schauer.
\newblock Conditioning continuous-time Markov processes by guiding, 2021,
\newblock Stochastics 95(6), 2022, pp. 963-996;
\newblock arXiv:2111.11377.
\newblock DOI: 10.1080/17442508.2022.215008.

\bibitem{2110.00602}
Chad Scherrer and Moritz Schauer.
\newblock Applied Measure Theory for Probabilistic Modeling, 2021,
\newblock JuliaCon Proceedings, 1(1), 92 (2022);
\newblock arXiv:2110.00602.
\newblock DOI: 10.21105/jcon.00092.

\bibitem{2108.11891}
Denis Belomestny, Shota Gugushvili, Moritz Schauer and Peter Spreij.
\newblock Weak solutions to gamma-driven stochastic differential equations, 2021,
\newblock Indagationes Mathematicae 34(4), pp. 820-829 (July 2023);
\newblock arXiv:2108.11891.
\newblock DOI: 10.1016/j.indag.2023.03.004.

\bibitem{2103.08478}
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen and Moritz Schauer.
\newblock Sticky PDMP samplers for sparse and local inference problems, 2021,
\newblock Stat Comput 33, 8 (2023);
\newblock arXiv:2103.08478.
\newblock DOI: 10.1007/s11222-022-10180-5.

\bibitem{2011.08321}
Denis Belomestny, Shota Gugushvili, Moritz Schauer and Peter Spreij.
\newblock Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations, 2020,
\newblock Bernoulli 28(4), 2022, pp. 2151-2180;
\newblock arXiv:2011.08321.
\newblock DOI: 10.3150/21-BEJ1413.

\bibitem{2002.00885}
Alexis Arnaudon, Frank van der Meulen, Moritz Schauer and Stefan Sommer.
\newblock Diffusion bridges for stochastic Hamiltonian systems and shape evolutions, 2020,
\newblock SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323;
\newblock arXiv:2002.00885.
\newblock DOI: 10.1137/21M1406283.

\bibitem{2001.05889}
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen and Moritz Schauer.
\newblock A piecewise deterministic Monte Carlo method for diffusion bridges, 2020,
\newblock Stat Comput 31, 37 (2021);
\newblock arXiv:2001.05889.
\newblock DOI: 10.1007/s11222-021-10008-8.

\bibitem{1810.07668}
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter Spreij.
\newblock Bayesian wavelet de-noising with the caravan prior, 2018,
\newblock ESAIM Probab. Stat., Volume 23, pages 947-978, 2019;
\newblock arXiv:1810.07668.
\newblock DOI: 10.1051/ps/2019019.

\bibitem{1810.01761}
Joris Bierkens, Frank van der Meulen and Moritz Schauer.
\newblock Simulation of elliptic and hypo-elliptic conditional diffusions, 2018,
\newblock Adv. Appl. Probab. 52 (2020) 173-212;
\newblock arXiv:1810.01761.
\newblock DOI: 10.1017/apr.2019.54.

\bibitem{1805.05606}
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter Spreij.
\newblock Nonparametric Bayesian volatility learning under microstructure noise, 2018,
\newblock Jpn. J. Stat. Data. Sci 6, 551-571 (2023);
\newblock arXiv:1805.05606.
\newblock DOI: 10.1007/s42081-022-00185-9.

\bibitem{1804.11267}
Denis Belomestny, Shota Gugushvili, Moritz Schauer and Peter Spreij.
\newblock Nonparametric Bayesian inference for Gamma-type L\'evy subordinators, 2018,
\newblock Communications in Mathematical Sciences, Volume 17, Number 3, 2019;
\newblock arXiv:1804.11267.
\newblock DOI: 10.4310/CMS.2019.v17.n3.a8.

\bibitem{1804.03616}
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter Spreij.
\newblock Fast and scalable non-parametric Bayesian inference for Poisson point processes, 2018,
\newblock RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6;
\newblock arXiv:1804.03616.

\bibitem{1801.09956}
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter Spreij.
\newblock Nonparametric Bayesian volatility estimation, 2018,
\newblock 2017 MATRIX Annals, Springer International Publishing, 2019;
\newblock arXiv:1801.09956.
\newblock DOI: 10.1007/978-3-030-04161-8_19.

\bibitem{1712.03807}
Marcin Mider, Moritz Schauer and Frank van der Meulen.
\newblock Continuous-discrete smoothing of diffusions, 2017,
\newblock Electron. J. Statist. 15(2): 4295-4342 (2021);
\newblock arXiv:1712.03807.
\newblock DOI: 10.1214/21-EJS1894.

\bibitem{1706.07449}
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter Spreij.
\newblock Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient, 2017,
\newblock Braz. J. Probab. Stat., 34(3): 537-579. 2020;
\newblock arXiv:1706.07449.
\newblock DOI: 10.1214/19-BJPS433.

\bibitem{1612.05124}
Frank van der Meulen, Moritz Schauer and Jan van Waaij.
\newblock Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions, 2016,
\newblock J. Stat Inference Stoch Process (2018) 21: 603;
\newblock arXiv:1612.05124.
\newblock DOI: 10.1007/s11203-017-9163-7.

\bibitem{1606.04082}
Frank van der Meulen and Moritz Schauer.
\newblock Bayesian estimation of incompletely observed diffusions, 2016,
\newblock Stochastics 90 (5), 2018, pp. 641-662;
\newblock arXiv:1606.04082.
\newblock DOI: 10.1080/17442508.2017.1381097.

\bibitem{1406.4704}
Frank van der Meulen and Moritz Schauer.
\newblock Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals, 2014,
\newblock Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396;
\newblock arXiv:1406.4704.
\newblock DOI: 10.1214/17-EJS1290.

\bibitem{1311.3606}
Moritz Schauer, Frank van der Meulen and Harry van Zanten.
\newblock Guided proposals for simulating multi-dimensional diffusion bridges, 2013,
\newblock Bernoulli Volume 23, Number 4A (November 2017), 2917-2950;
\newblock arXiv:1311.3606.
\newblock DOI: 10.3150/16-BEJ833.

\bibitem{1206.4910}
Frank van der Meulen, Moritz Schauer and Harry van Zanten.
\newblock Reversible jump MCMC for nonparametric drift estimation for diffusion processes, 2012,
\newblock Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014);
\newblock arXiv:1206.4910.
\newblock DOI: 10.1016/j.csda.2013.03.002.

\end{thebibliography}

Preprints:

Please add the journal reference and DOI for your papers as soon as they are published.

<ul>
<li>
Elizabeth L. Baker, Moritz Schauer, Stefan Sommer: “Score matching for bridges without time-reversals”, 2024; <a href='http://arxiv.org/abs/2407.15455'>arXiv:2407.15455</a>.
</li><li>
Gaurav Arya, Moritz Schauer, Ruben Seyer: “Gradient Estimation via Differentiable Metropolis-Hastings”, 2024; <a href='http://arxiv.org/abs/2406.14451'>arXiv:2406.14451</a>.
</li><li>
Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Simulating conditioned diffusions on manifolds”, 2024; <a href='http://arxiv.org/abs/2403.05409'>arXiv:2403.05409</a>.
</li><li>
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer: “Differentiating Metropolis-Hastings to Optimize Intractable Densities”, 2023; <a href='http://arxiv.org/abs/2306.07961'>arXiv:2306.07961</a>.
</li><li>
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts, Moritz Schauer: “Methods and applications of PDMP samplers with boundary conditions”, 2023; <a href='http://arxiv.org/abs/2303.08023'>arXiv:2303.08023</a>.
</li><li>
Gaurav Arya, Moritz Schauer, Frank Schäfer, Chris Rackauckas: “Automatic Differentiation of Programs with Discrete Randomness”, 2022; <a href='http://arxiv.org/abs/2210.08572'>arXiv:2210.08572</a>.
</li><li>
Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer: “Flexible Group Fairness Metrics for Survival Analysis”, 2022; <a href='http://arxiv.org/abs/2206.03256'>arXiv:2206.03256</a>.
</li><li>
Frank van der Meulen, Moritz Schauer: “Automatic Backward Filtering Forward Guiding for Markov processes and graphical models”, 2020; <a href='http://arxiv.org/abs/2010.03509'>arXiv:2010.03509</a>.
</li><li>
Richard C. Kraaij, Moritz Schauer: “A generator approach to stochastic monotonicity and propagation of order”, 2018; <a href='http://arxiv.org/abs/1804.10222'>arXiv:1804.10222</a>.
</li><li>
Frank van der Meulen, Moritz Schauer: “On residual and guided proposals for diffusion bridge simulation”, 2017; <a href='http://arxiv.org/abs/1708.04870'>arXiv:1708.04870</a>.
</li>
</ul>

Published:

<ul>
<li>
Moritz Schauer, Marcel Wienöbst: “Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs”, 2023, Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024; <a href='http://arxiv.org/abs/2310.05655'>arXiv:2310.05655</a>.
</li><li>
Marc Corstanje, Frank van der Meulen, Moritz Schauer: “Conditioning continuous-time Markov processes by guiding”, 2021, Stochastics 95(6), 2022, pp. 963-996; <a href='http://arxiv.org/abs/2111.11377'>arXiv:2111.11377</a>. DOI: <a href='https://dx.doi.org/10.1080/17442508.2022.215008'>10.1080/17442508.2022.215008</a>.
</li><li>
Chad Scherrer, Moritz Schauer: “Applied Measure Theory for Probabilistic Modeling”, 2021, JuliaCon Proceedings, 1(1), 92 (2022); <a href='http://arxiv.org/abs/2110.00602'>arXiv:2110.00602</a>. DOI: <a href='https://dx.doi.org/10.21105/jcon.00092'>10.21105/jcon.00092</a>.
</li><li>
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Weak solutions to gamma-driven stochastic differential equations”, 2021, Indagationes Mathematicae 34(4), pp. 820-829 (July 2023); <a href='http://arxiv.org/abs/2108.11891'>arXiv:2108.11891</a>. DOI: <a href='https://dx.doi.org/10.1016/j.indag.2023.03.004'>10.1016/j.indag.2023.03.004</a>.
</li><li>
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “Sticky PDMP samplers for sparse and local inference problems”, 2021, Stat Comput 33, 8 (2023); <a href='http://arxiv.org/abs/2103.08478'>arXiv:2103.08478</a>. DOI: <a href='https://dx.doi.org/10.1007/s11222-022-10180-5'>10.1007/s11222-022-10180-5</a>.
</li><li>
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations”, 2020, Bernoulli 28(4), 2022, pp. 2151-2180; <a href='http://arxiv.org/abs/2011.08321'>arXiv:2011.08321</a>. DOI: <a href='https://dx.doi.org/10.3150/21-BEJ1413'>10.3150/21-BEJ1413</a>.
</li><li>
Alexis Arnaudon, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Diffusion bridges for stochastic Hamiltonian systems and shape evolutions”, 2020, SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323; <a href='http://arxiv.org/abs/2002.00885'>arXiv:2002.00885</a>. DOI: <a href='https://dx.doi.org/10.1137/21M1406283'>10.1137/21M1406283</a>.
</li><li>
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “A piecewise deterministic Monte Carlo method for diffusion bridges”, 2020, Stat Comput 31, 37 (2021); <a href='http://arxiv.org/abs/2001.05889'>arXiv:2001.05889</a>. DOI: <a href='https://dx.doi.org/10.1007/s11222-021-10008-8'>10.1007/s11222-021-10008-8</a>.
</li><li>
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Bayesian wavelet de-noising with the caravan prior”, 2018, ESAIM Probab. Stat., Volume 23, pages 947-978, 2019; <a href='http://arxiv.org/abs/1810.07668'>arXiv:1810.07668</a>. DOI: <a href='https://dx.doi.org/10.1051/ps/2019019'>10.1051/ps/2019019</a>.
</li><li>
Joris Bierkens, Frank van der Meulen, Moritz Schauer: “Simulation of elliptic and hypo-elliptic conditional diffusions”, 2018, Adv. Appl. Probab. 52 (2020) 173-212; <a href='http://arxiv.org/abs/1810.01761'>arXiv:1810.01761</a>. DOI: <a href='https://dx.doi.org/10.1017/apr.2019.54'>10.1017/apr.2019.54</a>.
</li><li>
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility learning under microstructure noise”, 2018, Jpn. J. Stat. Data. Sci 6, 551-571 (2023); <a href='http://arxiv.org/abs/1805.05606'>arXiv:1805.05606</a>. DOI: <a href='https://dx.doi.org/10.1007/s42081-022-00185-9'>10.1007/s42081-022-00185-9</a>.
</li><li>
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian inference for Gamma-type L\'evy subordinators”, 2018, Communications in Mathematical Sciences, Volume 17, Number 3, 2019; <a href='http://arxiv.org/abs/1804.11267'>arXiv:1804.11267</a>. DOI: <a href='https://dx.doi.org/10.4310/CMS.2019.v17.n3.a8'>10.4310/CMS.2019.v17.n3.a8</a>.
</li><li>
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Fast and scalable non-parametric Bayesian inference for Poisson point processes”, 2018, RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6; <a href='http://arxiv.org/abs/1804.03616'>arXiv:1804.03616</a>.
</li><li>
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation”, 2018, 2017 MATRIX Annals, Springer International Publishing, 2019; <a href='http://arxiv.org/abs/1801.09956'>arXiv:1801.09956</a>. DOI: <a href='https://dx.doi.org/10.1007/978-3-030-04161-8_19'>10.1007/978-3-030-04161-8_19</a>.
</li><li>
Marcin Mider, Moritz Schauer, Frank van der Meulen: “Continuous-discrete smoothing of diffusions”, 2017, Electron. J. Statist. 15(2): 4295-4342 (2021); <a href='http://arxiv.org/abs/1712.03807'>arXiv:1712.03807</a>. DOI: <a href='https://dx.doi.org/10.1214/21-EJS1894'>10.1214/21-EJS1894</a>.
</li><li>
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient”, 2017, Braz. J. Probab. Stat., 34(3): 537-579. 2020; <a href='http://arxiv.org/abs/1706.07449'>arXiv:1706.07449</a>. DOI: <a href='https://dx.doi.org/10.1214/19-BJPS433'>10.1214/19-BJPS433</a>.
</li><li>
Frank van der Meulen, Moritz Schauer, Jan van Waaij: “Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions”, 2016, J. Stat Inference Stoch Process (2018) 21: 603; <a href='http://arxiv.org/abs/1612.05124'>arXiv:1612.05124</a>. DOI: <a href='https://dx.doi.org/10.1007/s11203-017-9163-7'>10.1007/s11203-017-9163-7</a>.
</li><li>
Frank van der Meulen, Moritz Schauer: “Bayesian estimation of incompletely observed diffusions”, 2016, Stochastics 90 (5), 2018, pp. 641-662; <a href='http://arxiv.org/abs/1606.04082'>arXiv:1606.04082</a>. DOI: <a href='https://dx.doi.org/10.1080/17442508.2017.1381097'>10.1080/17442508.2017.1381097</a>.
</li><li>
Frank van der Meulen, Moritz Schauer: “Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals”, 2014, Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396; <a href='http://arxiv.org/abs/1406.4704'>arXiv:1406.4704</a>. DOI: <a href='https://dx.doi.org/10.1214/17-EJS1290'>10.1214/17-EJS1290</a>.
</li><li>
Moritz Schauer, Frank van der Meulen, Harry van Zanten: “Guided proposals for simulating multi-dimensional diffusion bridges”, 2013, Bernoulli Volume 23, Number 4A (November 2017), 2917-2950; <a href='http://arxiv.org/abs/1311.3606'>arXiv:1311.3606</a>. DOI: <a href='https://dx.doi.org/10.3150/16-BEJ833'>10.3150/16-BEJ833</a>.
</li><li>
Frank van der Meulen, Moritz Schauer, Harry van Zanten: “Reversible jump MCMC for nonparametric drift estimation for diffusion processes”, 2012, Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014); <a href='http://arxiv.org/abs/1206.4910'>arXiv:1206.4910</a>. DOI: <a href='https://dx.doi.org/10.1016/j.csda.2013.03.002'>10.1016/j.csda.2013.03.002</a>.
</li>
</ul>

Preprints:

Please add the journal reference and DOI for your papers as soon as they are published.

Preview:

Elizabeth L. Baker, Moritz Schauer, Stefan Sommer: “Score matching for bridges without time-reversals”, 2024; arXiv:2407.15455.
Gaurav Arya, Moritz Schauer, Ruben Seyer: “Gradient Estimation via Differentiable Metropolis-Hastings”, 2024; arXiv:2406.14451.
Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Simulating conditioned diffusions on manifolds”, 2024; arXiv:2403.05409.
Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer: “Differentiating Metropolis-Hastings to Optimize Intractable Densities”, 2023; arXiv:2306.07961.
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts, Moritz Schauer: “Methods and applications of PDMP samplers with boundary conditions”, 2023; arXiv:2303.08023.
Gaurav Arya, Moritz Schauer, Frank Schäfer, Chris Rackauckas: “Automatic Differentiation of Programs with Discrete Randomness”, 2022; arXiv:2210.08572.
Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer: “Flexible Group Fairness Metrics for Survival Analysis”, 2022; arXiv:2206.03256.
Frank van der Meulen, Moritz Schauer: “Automatic Backward Filtering Forward Guiding for Markov processes and graphical models”, 2020; arXiv:2010.03509.
Richard C. Kraaij, Moritz Schauer: “A generator approach to stochastic monotonicity and propagation of order”, 2018; arXiv:1804.10222.
Frank van der Meulen, Moritz Schauer: “On residual and guided proposals for diffusion bridge simulation”, 2017; arXiv:1708.04870.

For copy and pasting to a Wiki:

* Elizabeth L. Baker, Moritz Schauer, Stefan Sommer: “Score matching for bridges without time-reversals”, 2024; [http://arxiv.org/abs/2407.15455 arXiv:2407.15455].

* Gaurav Arya, Moritz Schauer, Ruben Seyer: “Gradient Estimation via Differentiable Metropolis-Hastings”, 2024; [http://arxiv.org/abs/2406.14451 arXiv:2406.14451].

* Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Simulating conditioned diffusions on manifolds”, 2024; [http://arxiv.org/abs/2403.05409 arXiv:2403.05409].

* Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer: “Differentiating Metropolis-Hastings to Optimize Intractable Densities”, 2023; [http://arxiv.org/abs/2306.07961 arXiv:2306.07961].

* Joris Bierkens, Sebastiano Grazzi, Gareth Roberts, Moritz Schauer: “Methods and applications of PDMP samplers with boundary conditions”, 2023; [http://arxiv.org/abs/2303.08023 arXiv:2303.08023].

* Gaurav Arya, Moritz Schauer, Frank Schäfer, Chris Rackauckas: “Automatic Differentiation of Programs with Discrete Randomness”, 2022; [http://arxiv.org/abs/2210.08572 arXiv:2210.08572].

* Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer: “Flexible Group Fairness Metrics for Survival Analysis”, 2022; [http://arxiv.org/abs/2206.03256 arXiv:2206.03256].

* Frank van der Meulen, Moritz Schauer: “Automatic Backward Filtering Forward Guiding for Markov processes and graphical models”, 2020; [http://arxiv.org/abs/2010.03509 arXiv:2010.03509].

* Richard C. Kraaij, Moritz Schauer: “A generator approach to stochastic monotonicity and propagation of order”, 2018; [http://arxiv.org/abs/1804.10222 arXiv:1804.10222].

* Frank van der Meulen, Moritz Schauer: “On residual and guided proposals for diffusion bridge simulation”, 2017; [http://arxiv.org/abs/1708.04870 arXiv:1708.04870].

Published:

Preview:

Moritz Schauer, Marcel Wienöbst: “Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs”, 2023, Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024; arXiv:2310.05655.
Marc Corstanje, Frank van der Meulen, Moritz Schauer: “Conditioning continuous-time Markov processes by guiding”, 2021, Stochastics 95(6), 2022, pp. 963-996; arXiv:2111.11377. DOI: 10.1080/17442508.2022.215008.
Chad Scherrer, Moritz Schauer: “Applied Measure Theory for Probabilistic Modeling”, 2021, JuliaCon Proceedings, 1(1), 92 (2022); arXiv:2110.00602. DOI: 10.21105/jcon.00092.
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Weak solutions to gamma-driven stochastic differential equations”, 2021, Indagationes Mathematicae 34(4), pp. 820-829 (July 2023); arXiv:2108.11891. DOI: 10.1016/j.indag.2023.03.004.
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “Sticky PDMP samplers for sparse and local inference problems”, 2021, Stat Comput 33, 8 (2023); arXiv:2103.08478. DOI: 10.1007/s11222-022-10180-5.
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations”, 2020, Bernoulli 28(4), 2022, pp. 2151-2180; arXiv:2011.08321. DOI: 10.3150/21-BEJ1413.
Alexis Arnaudon, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Diffusion bridges for stochastic Hamiltonian systems and shape evolutions”, 2020, SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323; arXiv:2002.00885. DOI: 10.1137/21M1406283.
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “A piecewise deterministic Monte Carlo method for diffusion bridges”, 2020, Stat Comput 31, 37 (2021); arXiv:2001.05889. DOI: 10.1007/s11222-021-10008-8.
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Bayesian wavelet de-noising with the caravan prior”, 2018, ESAIM Probab. Stat., Volume 23, pages 947-978, 2019; arXiv:1810.07668. DOI: 10.1051/ps/2019019.
Joris Bierkens, Frank van der Meulen, Moritz Schauer: “Simulation of elliptic and hypo-elliptic conditional diffusions”, 2018, Adv. Appl. Probab. 52 (2020) 173-212; arXiv:1810.01761. DOI: 10.1017/apr.2019.54.
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility learning under microstructure noise”, 2018, Jpn. J. Stat. Data. Sci 6, 551-571 (2023); arXiv:1805.05606. DOI: 10.1007/s42081-022-00185-9.
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian inference for Gamma-type L\'evy subordinators”, 2018, Communications in Mathematical Sciences, Volume 17, Number 3, 2019; arXiv:1804.11267. DOI: 10.4310/CMS.2019.v17.n3.a8.
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Fast and scalable non-parametric Bayesian inference for Poisson point processes”, 2018, RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6; arXiv:1804.03616.
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation”, 2018, 2017 MATRIX Annals, Springer International Publishing, 2019; arXiv:1801.09956. DOI: 10.1007/978-3-030-04161-8_19.
Marcin Mider, Moritz Schauer, Frank van der Meulen: “Continuous-discrete smoothing of diffusions”, 2017, Electron. J. Statist. 15(2): 4295-4342 (2021); arXiv:1712.03807. DOI: 10.1214/21-EJS1894.
Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient”, 2017, Braz. J. Probab. Stat., 34(3): 537-579. 2020; arXiv:1706.07449. DOI: 10.1214/19-BJPS433.
Frank van der Meulen, Moritz Schauer, Jan van Waaij: “Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions”, 2016, J. Stat Inference Stoch Process (2018) 21: 603; arXiv:1612.05124. DOI: 10.1007/s11203-017-9163-7.
Frank van der Meulen, Moritz Schauer: “Bayesian estimation of incompletely observed diffusions”, 2016, Stochastics 90 (5), 2018, pp. 641-662; arXiv:1606.04082. DOI: 10.1080/17442508.2017.1381097.
Frank van der Meulen, Moritz Schauer: “Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals”, 2014, Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396; arXiv:1406.4704. DOI: 10.1214/17-EJS1290.
Moritz Schauer, Frank van der Meulen, Harry van Zanten: “Guided proposals for simulating multi-dimensional diffusion bridges”, 2013, Bernoulli Volume 23, Number 4A (November 2017), 2917-2950; arXiv:1311.3606. DOI: 10.3150/16-BEJ833.
Frank van der Meulen, Moritz Schauer, Harry van Zanten: “Reversible jump MCMC for nonparametric drift estimation for diffusion processes”, 2012, Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014); arXiv:1206.4910. DOI: 10.1016/j.csda.2013.03.002.

For copy and pasting to a Wiki:

* Moritz Schauer, Marcel Wienöbst: “Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs”, 2023, Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024; [http://arxiv.org/abs/2310.05655 arXiv:2310.05655].

* Marc Corstanje, Frank van der Meulen, Moritz Schauer: “Conditioning continuous-time Markov processes by guiding”, 2021, Stochastics 95(6), 2022, pp. 963-996; [http://arxiv.org/abs/2111.11377 arXiv:2111.11377]. DOI: [https://dx.doi.org/10.1080/17442508.2022.215008 10.1080/17442508.2022.215008].

* Chad Scherrer, Moritz Schauer: “Applied Measure Theory for Probabilistic Modeling”, 2021, JuliaCon Proceedings, 1(1), 92 (2022); [http://arxiv.org/abs/2110.00602 arXiv:2110.00602]. DOI: [https://dx.doi.org/10.21105/jcon.00092 10.21105/jcon.00092].

* Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Weak solutions to gamma-driven stochastic differential equations”, 2021, Indagationes Mathematicae 34(4), pp. 820-829 (July 2023); [http://arxiv.org/abs/2108.11891 arXiv:2108.11891]. DOI: [https://dx.doi.org/10.1016/j.indag.2023.03.004 10.1016/j.indag.2023.03.004].

* Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “Sticky PDMP samplers for sparse and local inference problems”, 2021, Stat Comput 33, 8 (2023); [http://arxiv.org/abs/2103.08478 arXiv:2103.08478]. DOI: [https://dx.doi.org/10.1007/s11222-022-10180-5 10.1007/s11222-022-10180-5].

* Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations”, 2020, Bernoulli 28(4), 2022, pp. 2151-2180; [http://arxiv.org/abs/2011.08321 arXiv:2011.08321]. DOI: [https://dx.doi.org/10.3150/21-BEJ1413 10.3150/21-BEJ1413].

* Alexis Arnaudon, Frank van der Meulen, Moritz Schauer, Stefan Sommer: “Diffusion bridges for stochastic Hamiltonian systems and shape evolutions”, 2020, SIAM Journal on Imaging Sciences 15 (1), 2022, pp. 293-323; [http://arxiv.org/abs/2002.00885 arXiv:2002.00885]. DOI: [https://dx.doi.org/10.1137/21M1406283 10.1137/21M1406283].

* Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: “A piecewise deterministic Monte Carlo method for diffusion bridges”, 2020, Stat Comput 31, 37 (2021); [http://arxiv.org/abs/2001.05889 arXiv:2001.05889]. DOI: [https://dx.doi.org/10.1007/s11222-021-10008-8 10.1007/s11222-021-10008-8].

* Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Bayesian wavelet de-noising with the caravan prior”, 2018, ESAIM Probab. Stat., Volume 23, pages 947-978, 2019; [http://arxiv.org/abs/1810.07668 arXiv:1810.07668]. DOI: [https://dx.doi.org/10.1051/ps/2019019 10.1051/ps/2019019].

* Joris Bierkens, Frank van der Meulen, Moritz Schauer: “Simulation of elliptic and hypo-elliptic conditional diffusions”, 2018, Adv. Appl. Probab. 52 (2020) 173-212; [http://arxiv.org/abs/1810.01761 arXiv:1810.01761]. DOI: [https://dx.doi.org/10.1017/apr.2019.54 10.1017/apr.2019.54].

* Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility learning under microstructure noise”, 2018, Jpn. J. Stat. Data. Sci 6, 551-571 (2023); [http://arxiv.org/abs/1805.05606 arXiv:1805.05606]. DOI: [https://dx.doi.org/10.1007/s42081-022-00185-9 10.1007/s42081-022-00185-9].

* Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian inference for Gamma-type L\'evy subordinators”, 2018, Communications in Mathematical Sciences, Volume 17, Number 3, 2019; [http://arxiv.org/abs/1804.11267 arXiv:1804.11267]. DOI: [https://dx.doi.org/10.4310/CMS.2019.v17.n3.a8 10.4310/CMS.2019.v17.n3.a8].

* Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Fast and scalable non-parametric Bayesian inference for Poisson point processes”, 2018, RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6; [http://arxiv.org/abs/1804.03616 arXiv:1804.03616].

* Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian volatility estimation”, 2018, 2017 MATRIX Annals, Springer International Publishing, 2019; [http://arxiv.org/abs/1801.09956 arXiv:1801.09956]. DOI: [https://dx.doi.org/10.1007/978-3-030-04161-8_19 10.1007/978-3-030-04161-8_19].

* Marcin Mider, Moritz Schauer, Frank van der Meulen: “Continuous-discrete smoothing of diffusions”, 2017, Electron. J. Statist. 15(2): 4295-4342 (2021); [http://arxiv.org/abs/1712.03807 arXiv:1712.03807]. DOI: [https://dx.doi.org/10.1214/21-EJS1894 10.1214/21-EJS1894].

* Shota Gugushvili, Frank van der Meulen, Moritz Schauer, Peter Spreij: “Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient”, 2017, Braz. J. Probab. Stat., 34(3): 537-579. 2020; [http://arxiv.org/abs/1706.07449 arXiv:1706.07449]. DOI: [https://dx.doi.org/10.1214/19-BJPS433 10.1214/19-BJPS433].

* Frank van der Meulen, Moritz Schauer, Jan van Waaij: “Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions”, 2016, J. Stat Inference Stoch Process (2018) 21: 603; [http://arxiv.org/abs/1612.05124 arXiv:1612.05124]. DOI: [https://dx.doi.org/10.1007/s11203-017-9163-7 10.1007/s11203-017-9163-7].

* Frank van der Meulen, Moritz Schauer: “Bayesian estimation of incompletely observed diffusions”, 2016, Stochastics 90 (5), 2018, pp. 641-662; [http://arxiv.org/abs/1606.04082 arXiv:1606.04082]. DOI: [https://dx.doi.org/10.1080/17442508.2017.1381097 10.1080/17442508.2017.1381097].

* Frank van der Meulen, Moritz Schauer: “Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals”, 2014, Electron. J. Statist. Volume 11, Number 1 (2017), 2358-2396; [http://arxiv.org/abs/1406.4704 arXiv:1406.4704]. DOI: [https://dx.doi.org/10.1214/17-EJS1290 10.1214/17-EJS1290].

* Moritz Schauer, Frank van der Meulen, Harry van Zanten: “Guided proposals for simulating multi-dimensional diffusion bridges”, 2013, Bernoulli Volume 23, Number 4A (November 2017), 2917-2950; [http://arxiv.org/abs/1311.3606 arXiv:1311.3606]. DOI: [https://dx.doi.org/10.3150/16-BEJ833 10.3150/16-BEJ833].

* Frank van der Meulen, Moritz Schauer, Harry van Zanten: “Reversible jump MCMC for nonparametric drift estimation for diffusion processes”, 2012, Computational Statistics & Data Analysis, Volume 71, Pages 615-632, ISSN 0167-9473 (2014); [http://arxiv.org/abs/1206.4910 arXiv:1206.4910]. DOI: [https://dx.doi.org/10.1016/j.csda.2013.03.002 10.1016/j.csda.2013.03.002].

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