Department of Mathematical Sciences at Chalmers University of Technology and University of Gothenburg, Sweden.

Moritz Schauer

Moritz Schauer
Associate Senior Lecturer (biträdande universitetslektor)
Department of Mathematical Sciences

Chalmers University of Technology | University of Gothenburg

Contact/E-mail

Office: Chalmers Tvärgata 3, Room H3029, 41296 Göteborg (map)

Phone: +46 31 772 3029

Mail: smoritz@chalmers.se

University of Gothenburg Address Book / Staff Page Mathematical Sciences

Stations

2019 –   Associate senior lecturer Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg.
2015 – 2019  Postdoc at the Mathematical Institute, University of Leiden. Projects: Causal Discovery from High-Dimensional Data in the Large-Sample Limit
2014 – 2015  Postdoc at the Korteweg-de Vries Institute for Mathematics, University of Amsterdam, VICI project Foundations of nonparametric Bayes procedures.
2010 – 2014  PhD candidate at the Delft Institute of Applied Mathematics, Delft University of Technology in cooperation with EURANDOM and support by the STAR cluster of the Dutch Science Foundation NWO.
2004 – 2009  Diplom-Mathematik at University of Hamburg, Department Mathematical Statistics and Stochastic Processes.

Research interest

Nonparametric Bayesian inference for diffusion processes.

Conditional diffusion processes / diffusion bridges.

Bayesian inference on graphs and causal inference.

Journal

Publications

Preprints

Joris Bierkens, Frank van der Meulen, Moritz Schauer: Simulation of elliptic and hypo-elliptic conditional diffusions. arxiv:1810.01761, 2018.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Nonparametric Bayesian volatility learning under microstructure noise. arxiv:1805.05606, 2018.

Richard C. Kraaij, Moritz Schauer: A generator approach to stochastic monotonicity and propagation of order. arxiv:1804.10222, 2018.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Fast and scalable non-parametric Bayesian inference for Poisson point processes. arxiv:1804.03616, 2018. Requesting comments at researchers.one.

Frank van der Meulen, Moritz Schauer: Continuous-discrete smoothing of diffusions. arxiv:1712.03807, 2017.

Monography/Thesis

Moritz Schauer: Bayesian inference for discretely observed diffusion processes. Ph.D. Thesis. Delft University of Technology, 2015.

Articles

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Bayesian wavelet de-noising with the caravan prior. ESAIM: Probability and Statistics, to appear, arxiv:1810.07668.

Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: Nonparametric Bayesian inference for Gamma type Lévy subordinators. Communications in Mathematical Sciences 17 (3), 2019, pp. 781–816, 10.4310/CMS.2019.v17.n3.a8.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient.Brazilian Journal of Probability and Statistics, to appear, arxiv:1706.07449.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Nonparametric Bayesian volatility estimation. In: David R. Wood et al. (ed.): 2017 MATRIX Annals, Springer, 2019, ISBN 978-3-030-04160-1, 10.1007/978-3-030-04161-8_19.

Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes: Learning the causal structure of copula models with latent variables. In: 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. EID: 2-s2.0-85059432907.

Frank van der Meulen, Moritz Schauer: Bayesian estimation of incompletely observed diffusions. Stochastics 90 (5), 2018, pp. 641–662, 10.1080/17442508.2017.1381097.

Frank van der Meulen, Moritz Schauer, Jan van Waaij: Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions. Statistical Inference for Stochastic Processes 21 (3), 2018, 10.1007/s11203-017-9163-7.

Frank van der Meulen, Moritz Schauer: Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals. Electronic Journal of Statistics 11 (1), 2017, 10.1214/17-EJS1290.

Moritz Schauer, Frank van der Meulen, Harry van Zanten: Guided proposals for simulating multi-dimensional diffusion bridges. Bernoulli 23 (4A), 2017, pp. 2917–2950, 10.3150/16-BEJ833.

Frank van der Meulen, Moritz Schauer, Harry van Zanten: Reversible jump MCMC for nonparametric drift estimation for diffusion processes. Computational Statistics & Data Analysis 71, 2014, pp. 615–632, ISSN 0167-9473, 10.1016/j.csda.2013.03.002.

Christos Pelekis, Moritz Schauer: Network Coloring and Colored Coin Games. In: S. Alpern, R. Fokkink et al. (ed.): Search Theory: A Game Theoretic Perspective. Springer, 2013. ISBN-13: 978-146146824, 10.1007/978-1-4614-6825-7_4. Note: The proof therein is based on a uniform bound on the median of the number of sources (resp. sinks) in a graph with randomly oriented edges (randomly oriented graphs) of independent interest.

Software publications

Moritz Schauer et al.:Bridge 0.7. Zenodo, 10.5281/zenodo.1100978. A statistical toolbox for diffusion processes.

Shota Gugushvili, Moritz Schauer: MicrostructureNoise 0.10. Zenodo, 10.5281/zenodo.1241010. 2018. Bayesian volatility estimation in presence of market microstructure noise.

Moritz Schauer: CausalInference 0.4. Zenodo, 10.5281/zenodo.1005091. Julia package for causal inference, graphical models and structure learning with the PC algorithm.

Bibliography and author information

 arXiv  https://arxiv.org/a/0000-0003-3310-7915.html

  http://orcid.org/0000-0003-3310-7915

Preferred names in citations are “Moritz Schauer” and “M. Schauer”.

Download .bib-file

Open source contributions

See github.com/mschauer.