Moritz Schauer

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Frank van der MeulenVrije Universiteit AmsterdamVerified email at vu.nl
Peter SpreijUniversiteit van Amsterdam, Korteweg-de Vries Institute for MathematicsVerified email at uva.nl
Shota GugushviliWageningen University & ResearchVerified email at yesdatasolutions.com
Harry van ZantenVerified email at vu.nl
Joris BierkensTechnische Universiteit DelftVerified email at tudelft.nl
Sebastiano GrazziResearch Associate, Department of Statistics, University of WarwickVerified email at warwick.ac.uk
Frank SchäferMassachusetts Institute of TechnologyVerified email at mit.edu
Christopher Vincent RackauckasMassachusetts Institute of TechnologyVerified email at mit.edu
Prof. Dr. Denis BelomestnyDuisburg-Essen UniversityVerified email at uni-due.de
Stefan SommerProfessor at University of CopenhagenVerified email at di.ku.dk
Gaurav AryaMassachusetts Institute of TechnologyVerified email at mit.edu
Alexis ArnaudonEcole Polytechnique Federale de LausanneVerified email at epfl.ch
Jan van WaaijDepartment of Health Technology, Section for Bioinformatics, Danish University of TechnologyVerified email at dtu.dk
Christos PelekisAristotle University of ThessalonikiVerified email at math.auth.gr
Sebastian J. VollmerUniversity of Kaiserslautern/DFKIVerified email at cs.uni-kl.de
Raphael SonabendImperial College LondonVerified email at imperial.ac.uk
Alan MishlerJ.P. Morgan AI ResearchVerified email at alanmishler.com
Florian PfistererLudwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Alexander K. LewGraduate student, MITVerified email at mit.edu
Mathieu HuotResearch Scientist, MITVerified email at crans.org

Moritz Schauer

Chalmers University of Technology | University of Gothenburg

Verified email at chalmers.se - Homepage

Nonparametric Bayesian inference Boundary value problems for S(P)DEs Conditional diffusion processes


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Guided proposals for simulating multi-dimensional diffusion bridges M Schauer, F van der Meulen, H van Zanten Bernoulli 23 (4A), 2917–2950, 2017	65	2017
Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals F van der Meulen, M Schauer	60	2017
Reversible jump MCMC for nonparametric drift estimation for diffusion processes F van der Meulen, M Schauer, H van Zanten Computational Statistics & Data Analysis 71, 615-632, 2014	46	2014
Continuous-discrete smoothing of diffusions M Mider, M Schauer, F van der Meulen Electronic Journal of Statistics 15 (2), pp. 4295–4342, 2021	29*	2021
Bayesian estimation of incompletely observed diffusions F van der Meulen, M Schauer Stochastics 90 (5), 641-662, 2018	23	2018
Automatic Differentiation of Programs with Discrete Randomness G Arya, M Schauer, F Schäfer, C Rackauckas Advances in Neural Information Processing Systems, 2022	22	2022
Learning the causal structure of copula models with latent variables R Cui, P Groot, M Schauer, T Heskes Corvallis: AUAI Press, 2018	22	2018
A piecewise deterministic Monte Carlo method for diffusion bridges J Bierkens, S Grazzi, F van der Meulen, M Schauer Statistics and Computing 31, 2021	17	2021
Simulation of elliptic and hypo-elliptic conditional diffusions J Bierkens, F van der Meulen, M Schauer Advances in Applied Probability 52 (1), 173-212, 2020	17	2020
Fast and scalable non-parametric Bayesian inference for Poisson point processes S Gugushvili, F van der Meulen, M Schauer, P Spreij RESEARCHERS.ONE, 2019, https://www.researchers.one/article/2019-06-6, with …, 2019	17	2019
Diffusion bridges for stochastic Hamiltonian systems with applications to shape analysis A Arnaudon, F van der Meulen, M Schauer, S Sommer SIAM Journal on Imaging Sciences 15 (1), 293-323, 2022	15*	2022
Automatic backward filtering forward guiding for Markov processes and graphical models F Van der Meulen, M Schauer arXiv preprint arXiv:2010.03509, 2020	13	2020
Adaptive nonparametric drift estimation for diffusion processes using Faber-Schauder expansions F van der Meulen, M Schauer, J van Waaij Statistical Inference for Stochastic Processes 21 (3), 603-628, 2018	13	2018
Sticky PDMP samplers for sparse and local inference problems J Bierkens, S Grazzi, F Meulen, M Schauer Statistics and Computing 33 (1), 8, 2023	10	2023
Nonparametric Bayesian inference for Gamma-type Lévy subordinators D Belomestny, S Gugushvili, M Schauer, P Spreij Communications in Mathematical Sciences 17 (3), 781-816, 2019	8*	2019
Nonparametric Bayesian volatility estimation S Gugushvili, F der Meulen, M Schauer, P Spreij 2017 MATRIX annals, 279-302, 2019	7	2019
Network coloring and colored coin games C Pelekis, M Schauer Search Theory: A Game Theoretic Perspective, 59-73, 2013	7	2013
Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient S Gugushvili, F van der Meulen, M Schauer, P Spreij	6	2020
Nonparametric Bayesian volatility learning under microstructure noise S Gugushvili, F van der Meulen, M Schauer, P Spreij arXiv preprint arXiv:1805.05606, 2018	6*	2018
Flexible group fairness metrics for survival analysis R Sonabend, F Pfisterer, A Mishler, M Schauer, L Burk, S Mukherjee, ... DSHealth 2022 (Workshop on Applied Data Science for Healthcare); arXiv …, 2022	5	2022

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