Moritz Schauer

Stations

2019 –

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

2024 –	Universitetslektor, University of Gothenburg.
2023 –	Docent for Mathematical Statistics, University of Gothenburg.
2023 –	Advisory Committee, SciML.
2020 –	Collaborating researcher Chalmers AI Research Centre.
2019 –	Biträdande universitetslektor, 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

https://research.chalmers.se/person/smoritz

Nonparametric Bayesian inference for diffusion processes.

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

Bayesian inference on graphs and causal inference.

Publications

Forthcoming

Elizabeth L. Baker, Moritz Schauer, Stefan Sommer: Score matching for bridges without learning time-reversals. The 28th International Conference on Artificial Intelligence and Statistics. arXiv.2407.15455, 2025.

Preprints

Oskar Eklund, Annika Lang, Moritz Schauer: Guided smoothing and control for diffusion processes arXiv:2503.04326, 2025.

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.

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.

Monography/Thesis

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

Articles

2024

Moritz Schauer, Marcel Wienöbst: Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes. Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:382-400, 2024.

2023

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)

2022

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).

2021

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).

2020

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. https://nextjournal.com/Lobatto/FitzHugh-Nagumo, 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)

2019

Frank van der Meulen, Shota Gugushvili, Moritz Schauer, Peter Spreij: Fast and scalable non-parametric Bayesian inference for Poisson point processes. RESEARCHERS.ONE, 2019, https://www.researchers.one/article/2019-06-6, 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.

2018

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.

2017

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

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”. IPA: [ˈmoː/r/ɪts ˈʃaʊ̯ɐ].

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

Agenda

Gothenburg Statistics Seminar

Schedule hosted at researchseminars.org
Time			Speaker	Title
Wed	Mar 19	13:15	Adélie Erard	A method for estimating population growth at a local scale. An application using French breeding bird surveys data.
Wed	Apr 16	13:15	Vincent Molin	TBA
Wed	Apr 23	13:15	Sara Hamis	TBA
Tue	Apr 29	13:15	Alvaro Köhn-Luque	TBA
Wed	May 07	13:15	Calum Gabbutt	TBA
Tue	May 27	10:30	Nils Grimbeck	TBA
Wed	May 28	13:15	Mattias Byléhn	PhD defence
Wed	Jun 11	13:15	Elizabeth Baker	TBA

Open source contributions

See github.com/mschauer.

Supervision

Students

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 https://www.svalna.se.

Louise Hultén: Causal effect of carbon footprint calculators. 2022. In cooperation with https://www.svalna.se.

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

Siddhant Som, Swaathy Sambath: Scene Change Detection. 2022. https://hdl.handle.net/20.500.12380/304951. 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. https://hdl.handle.net/20.500.12380/302134, 2020. In cooperation with https://www.swerim.se.

Eva Hegnar: Probabilistic deep learning with variational inference. https://hdl.handle.net/20.500.12380/301602, 2020. In cooperation with https://www.solutionseeker.no.

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

Journal

2021/10/11 Nonparametric Bayes and Fourier methods in Julia (Intro Julia/Pluto notebook)
2021/07/30 ZigZagBoomerang.jl - parallel inference and variable selection (JuliaCon 2021 | Youtube)
2021/06/16 Sensitivity Analysis of Hybrid Differential Equations (with Frank Schäfer)
2018/05/17 MicrostructureNoise
2018/05/09 Talk: Continuous-discrete smoothing of diffusions (Mannheim Probability and Statistics Seminar)
2018/05/08 Talk: Nonparametric Bayesian volatility estimation (Statistik-Kolloquium der Universitäten Gießen und Marburg)
2018/01/31 Multivariate stochastic differential equations with Bridge
2018/01/19 Parameter inference for a simple SIR model