Associate professor Delft University of Technology Electrical Engineering, Mathematics and Computer Science Delft Institute of Applied Mathematics The Netherlands Phone: 015-2784517 Room: E-1.180 E-mail: f(dot)h(dot)vandermeulen(at)tudelft(dot)nl |
Some keywords: statistical inference for stochastic processes
(diffusions, Lévy processes); Bayesian computation; Bayesian
asymptotics; graphical models; dynamical systems;
longitudinal data.
If
we share research interests, feel free to send me an email to
discuss possibilities for collaboration.
J. Bierkens, S. Grazzi, F.H. van der
Meulen and M. Schauer (2021) Sticky PDMP samplers for
sparse and local inference problems, arXiv, submitted.
F.H. van der Meulen
and M. Schauer (2021) Automatic Backward Filtering Forward
Guiding for Markov processes and graphical models, working
paper, autobffg4.pdf
Updated March 3. In this paper we show that guided
proposals as defined in previous work for diffusions can be
defined for Bayesian networks and continuous time Markov
processes (different from diffusions). The guided processes
introduced in the paper are obtained by using an approximation
to Doob's h-transform. Furthermore, we fully explain the
compositional structure of the backward filtering
forward guiding algorithm.
M. Mider, M.R. Schauer and F.H. van der Meulen (2020) Continuous-discrete smoothing of diffusions, submitted, arXiv Fully revised version, good starting point if you are interested in inference for partially observed diffusions using backward filtering forward guiding.
A. Arnaudon, F.H. van der Meulen, M.R. Schauer and S. Sommer (2020) Diffusion bridges for stochastic Hamiltonian systems with applications to shape analysis, submitted, arXiv , submitted.
L. Mészáros, F.H. van der Meulen, G. Jongbloed and G. el Sarafy (2021) Climate change induced uncertainties in Phytoplankton spring bloom dynamics, submitted.
J. Bierkens, S.
Grazzi, F.H. van der Meulen and M. Schauer (2020) A piecewise
deterministic Monte Carlo method for diffusion bridges, arXiv,
submitted.
F.H. van der Meulen
and M.R. Schauer (2017) On residual and guided proposals for
diffusion bridge simulation arXiv
The main point is that guided proposals (as Moritz and I have
defined these) are not computationally excessive. See
our updated work (jointly with Marcin Mider)
"Continuous-discrete smoothing of diffusions". It is a bit
frustrating to see a wrong claim being copied by some authors.
R.B. Hageman, F.H. van
der Meulen, A. Rouhan and M.L. Kaminski (2018) Improved
Risk-Based Inspection planning through in-service Hull
Structure Monitoring of FPSO hulls, submitted.
24) S. Gugushvili, F.H. van der Meulen, M.R. Schauer and P. Spreij (2018) Nonparametric Bayesian volatility estimation arXiv, MATRIX Annals, Editors: David R. Wood, Jan de Gier, Cheryl E. Praeger, Terence Tao. MATRIX Book Series, Vol 2, Springer, to appear.
23) F.H. van der
Meulen and M.R. Schauer (2017) Bayesian
estimation of incompletely observed diffusions, Stochastics
90(5), 641-662.
22) F.H. van der
Meulen, M.R. Schauer, J. van Waaij (2017) Adaptive
nonparametric drift estimation for diffusion processes using
Faber-Schauder expansions, Statistical
Inference for Stochastic Processes 21(3), 603-628.
21) F.H. van
der Meulen and M.R. Schauer, M.R. (2017) Bayesian
estimation of discretely observed multi-dimensional
diffusion processes using guided proposals,
Electronic Journal of Statistics 11(1), 2358--2396.
20) M.R.
Schauer, F.H. van der Meulen and J.H. van Zanten (2017) Guided
proposals for simulating multi-dimensional diffusion bridges,
Bernoulli 23(4A), 2917--2950
19) Gugushvili, S., Van der Meulen, F.H. and Spreij, P.J. (2018) A non-parametric Bayesian approach to decompounding from high frequency data. Statistical Inference for Stochastic Processes, 21, 53-79.
18) Hartman, K.,
Wittich, A. Cai, J.J., Van der Meulen, F.H. and Azevedo, J.M.N.
(2016) Estimating the age of Rissos dolphins (Grampus
griseus) based on skin appearance. Journal of Mammology 97(2),
490--502.
17) Litvak, N. and Van der Meulen, F.H. (2015) Networks & Big Data. Nieuw Archief voor Wiskunde 5(2), 138--139.
16) Gugushvili, S., Spreij, P. and Van der Meulen, F.H. (2015) Non-parametric Bayesian inference for multi-dimensional compound Poisson processes. Modern Stochastics: Theory and Applications 2(1), 1--15.7) Jongbloed, G. and Van der Meulen, F.H. (2009) Estimating a concave distribution function from data corrupted with additive noise. Annals of Statistics 37(2), 782-815.
6) Vermaat, M.B., Van der Meulen, F.H. and Does, R.J.M.M. (2008) Asymptotic Behaviour of the Variance of the EWMA Statistic for Autoregressive Processes Statistics and Probability Letters 78(12), 1673-1682
5) Van der Meulen, F.H., Van der Vaart, A.W. and Van Zanten, J.H. (2006) Convergence rates of posterior distributions for Brownian semimartingale models Bernoulli 12(5), 863-888
4) Jongbloed, G. and Van der Meulen, F.H. (2006) Parametric estimation for subordinators and induced OU-processes Scandinavian Journal of Statistics 33(4), 825-847
3) Ramaker, H.JJ., Van Sprang, E.N.M., Westerhuis, J.A., Gorden, S.P., Van der Meulen, F.H., Smilde, A.K. (2006) Performance assessment and improvement of control charts for statistical batch process monitoring Statistica Neerlandica 60(3), 339-360
2) Jongbloed, G. Meulen, F.H. van der, Vaart, A.W. van der (2005) Nonparametric inference for Lévy driven Ornstein-Uhlenbeck processes. Bernoulli 11(5), 759-7912001-2005:
PhD student at Vrije Universiteit Amsterdam
2005-2007: Consultant/researcher at the Institute
for Business and Industrial Statistics at the University
of Amsterdam (IBIS UvA)
2007-2017: Assistant professor at TU Delft
2018-now: Associate professor at TU Delft
2012-now: Scientific advisor for company
ProjectsOne
I have taught
coursed in statistics, probability, analysis and linear
algebra in the bachelor and master for over 10 years. For
the courses financial time series (minor Finance at TU
Delft) and statistical inference (master course at TU
Delft) I have written lectures notes.
I enjoy
implementing new computational ideas, see my Github account.