| 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 | 39 | 2014 |
| Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals F van der Meulen, M Schauer Electronic Journal of Statistics 11 (1), 2358-2396, 2017 | 29 | 2017 |
| Guided proposals for simulating multi-dimensional diffusion bridges M Schauer, F van der Meulen, H van Zanten Bernoulli 23 (4A), 2917–2950, 2017 | 27 | 2017 |
| 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 | 11 | 2019 |
| Bayesian estimation of incompletely observed diffusions F van der Meulen, M Schauer Stochastics 90 (5), 641-662, 2018 | 10 | 2018 |
| 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 | 8 | 2018 |
| Learning the Causal Structure of Copula Models with Latent Variables. R Cui, P Groot, M Schauer, T Heskes UAI, 188-197, 2018 | 7 | 2018 |
| 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 | 5 | 2020 |
| Nonparametric Bayesian volatility estimation S Gugushvili, F van der Meulen, M Schauer, P Spreij 2017 MATRIX Annals, 279-302, 2019 | 4 | 2019 |
| Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient S Gugushvili, F van der Meulen, M Schauer, P Spreij Available at SSRN 3093100, 2018 | 4 | 2018 |
| On residual and guided proposals for diffusion bridge simulation F van der Meulen, M Schauer arXiv preprint arXiv:1708.04870, 2017 | 4 | 2017 |
| Network coloring and colored coin games C Pelekis, M Schauer Search Theory, 59-73, 2013 | 4 | 2013 |
| Continuous-discrete smoothing of diffusions M Mider, M Schauer, F van der Meulen arXiv preprint arXiv:1712.03807, 2020 | 2* | 2020 |
| Bayesian wavelet de-noising with the caravan prior S Gugushvili, F van der Meulen, M Schauer, P Spreij ESAIM: Probability and Statistics 23, 947-978, 2019 | 2 | 2019 |
| Nonparametric Bayesian inference for Gamma-type L\'evy subordinators D Belomestny, S Gugushvili, M Schauer, P Spreij Communications in Mathematical Sciences 17 (3), 781-816, 2019 | 2* | 2019 |
| Nonparametric Bayesian volatility learning under microstructure noise S Gugushvili, F van der Meulen, M Schauer, P Spreij Available at SSRN 3178606, 2018 | 2 | 2018 |
| Diffusion bridges for stochastic Hamiltonian systems with applications to shape analysis A Arnaudon, F van der Meulen, M Schauer, S Sommer arXiv preprint arXiv:2002.00885, 2020 | 1 | 2020 |
| A piecewise deterministic Monte Carlo method for diffusion bridges J Bierkens, S Grazzi, F van der Meulen, M Schauer arXiv preprint arXiv:2001.05889, 2020 | | 2020 |
| A generator approach to stochastic monotonicity and propagation of order RC Kraaij, M Schauer arXiv preprint arXiv:1804.10222, 2018 | | 2018 |
| Bayesian inference for discretely observed diffusion processes M Schauer Verlag Blaues Schloss, 2015 | | 2015 |
Frank Van Der MeulenDelft University of TechnologyVerified email at tudelft.nl
