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


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

Phone: +46 31 772 3029


Github: @mschauer

University of Gothenburg Address Book / Staff Page Mathematical Sciences


2019 –   Associate senior lecturer Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg.
2023 –   Docent for Mathematical Statistics, University of Gothenburg.
2023 –   Advisory Committee, SciML.
2020 –   Collaborating researcher Chalmers AI Research Centre.
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.

Boundary value problems for S(P)DEs and conditional diffusion processes.

Bayesian inference on graphs and causal inference.


PhD project: Stochastic Continuous-Depth Neural Networks. Chalmers AI Research Centre (CHAIR), 2020-2025.

Project Exploring and Statistically Learning an Excitable Stochastic-Dynamical Model. Scholarship for Explorable Research,, 2019-2020.



Gaurav Arya, Moritz Schauer, Ruben Seyer: Gradient Estimation via Differentiable Metropolis-Hastings. arXiv:2406.14451, 2024.

Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer: Simulating conditioned diffusions on manifolds. arXiv.2403.05409, 2024.

Moritz Schauer, Marcel Wienöbst: Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs. arXiv.2310.05655, 2023.

Joris Bierkens, Sebastiano Grazzi, Gareth Roberts, Moritz Schauer: Methods and applications of PDMP samplers with boundary conditions. arXiv:2303.08023, 2023.

Frank van der Meulen, Moritz Schauer: Automatic Backward Filtering Forward Guiding for Markov processes and graphical models. arXiv:2010.03509, 2020.

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


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



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 Differentiable Almost Everything Workshop of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, arXiv:2306.07961, 2023.

Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij: Weak solutions to gamma-driven stochastic differential equations. Indagationes Mathematicae 34 (4), 820-829, 10.1016/j.indag.2023.03.004 (arXiv:2108.11891), 2023.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Nonparametric Bayesian volatility learning under microstructure noise. Japanese Journal of Statistics and Data Science 6, pp. 551-571, 2023. 10.1007/s42081-022-00185-9 (arxiv:1805.05606).

Marc Corstanje, Frank van der Meulen, Moritz Schauer: Conditioning continuous-time Markov processes by guiding Stochastics 95 (6), 963--996, 2023. 10.1080/17442508.2022.2150081 (arXiv:2111.11377).

Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: Sticky PDMP samplers for sparse and local inference problems. Statistics and Computing 33 (1), 2023. 10.1007/s11222-022-10180-5 (arXiv:2103.08478)


Gaurav Arya, Moritz Schauer, Frank Schäfer, Chris Rackauckas: Automatic Differentiation of Programs with Discrete Randomness. Advances in Neural Information Processing Systems 35, 10435--10447, 2022.(Link, arXiv:2210.08572).

Alexis Arnaudon, Frank van der Meulen, Moritz Schauer, Stefan Sommer: Diffusion bridges for stochastic Hamiltonian systems with applications to shape analysis. SIAM Journal on Imaging Sciences 15:1, pp. 293–323, 2022. 10.1137/21M1406283 (arxiv:2002.00885).

Chad Scherrer, Moritz Schauer: Applied measure theory for probabilistic modeling. JuliaCon Proceedings, 1(1), 92, 2022. 10.21105/jcon.00092.

Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Sumantrak Mukherjee, Lukas Burk and Sebastian Vollmer: Flexible group fairness metrics for survival analysis. DSHealth 2022 (Workshop on Applied Data Science for Healthcare), 2022. (arxiv:2206.03256).

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


Marcin Mider, Moritz Schauer, Frank van der Meulen: Continuous-discrete smoothing of diffusions. Electronic Journal of Statistics 15 (2), pp. 4295–4342, 2021. 10.1214/21-EJS1894, (arxiv:1712.03807).

Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: A piecewise deterministic Monte Carlo method for diffusion bridges. Statistics and Computing 31, 2021, 10.1007/s11222-021-10008-8, (arxiv:2001.05889).


Joris Bierkens, Frank van der Meulen, Moritz Schauer: Simulation of elliptic and hypo-elliptic conditional diffusions. Advances in Applied Probability 52, pp. 173–212, 2020, 10.1017/apr.2019.54.

Sebastiano Grazzi, Marcin Mider, Frank van der Meulen, Moritz Schauer: Bayesian inference for SDE models: a case study for an excitable stochastic-dynamical model., Project: Nextjournal Scholarship for Explorable Research, 2020.

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 34 (3), pp. 537–579, 2020, 10.1214/19-BJPS433. (pdf)


Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Fast and scalable non-parametric Bayesian inference for Poisson point processes. RESEARCHERS.ONE, 2019,, with comments by Paulo Jorge de Andrade Serra, Ryan Martin and Adeline Samson.

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Bayesian wavelet de-noising with the caravan prior. ESAIM: Probability and Statistics 23, pp. 947–978, 2019, 10.1051/ps/2019019.

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

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.

Before 2015

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.

Selected software publications

Moritz Schauer et al.:Bridge. Zenodo, 10.5281/zenodo.891230. 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


Preferred names in citations are “Moritz Schauer” and “M. Schauer”. IPA: [ˈmoː/r/ɪts ˈʃaʊ̯ɐ].

Cite work from arXiv (.bib). Download .bib-file with information from .


Gothenburg Statistics Seminar

Open source contributions




Ruben Seyer, supervision.

Marc Corstanje, co-supervision.

Oskar Eklund, co-supervision.

Sebastiano Grazzi (TU Delft), ipso facto.

Google Summer of Code

Frank Schäfer: High weak order solvers and adjoint sensitivity analysis for stochastic differential equations. In: GSoC 2020 (Julialang). Report.

Frank Schäfer: Neural Hybrid Differential Equations and Adjoint Sensitivity Analysis. In: GSoC 2021 (Numfocus). Report.

Archana R Warrier: Causal and counterfactual methods for fairness in machine learning. In: GSoC 2021 (JuliaLang). Report.

Master theses

Hanna Skytt: Change point detection in financial time series in connection to purchase behaviours. 2022. In cooperation with

Louise Hultén: Causal effect of carbon footprint calculators. 2022. In cooperation with

Vincent Molin: Bayesian inverse problems with neural generative priors. 2022. With Axel Ringh.

Siddhant Som, Swaathy Sambath: Scene Change Detection. 2022. In cooperation with CEVT.

Noa Onoszko, Gustav Karlsson: PENS: Leveraging Data Heterogeneity in Federated Learning. 2021. In cooperation with: Edvin Listo Zec, RISE.

Krister Ekström: Multivariate linear regression of LIBS spectra., 2020. In cooperation with

Eva Hegnar: Probabilistic deep learning with variational inference., 2020. In cooperation with

Erik Hermansson: Latent State Estimation of Financial Time Series: estimating financial health with MCMC methods and particle filters. 2020.