Associate professor Delft University of Technology Electrical Engineering, Mathematics and Computer Science Delft Institute of Applied Mathematics Mekelweg 4, 2628CD Delft The Netherlands Phone: 015-2784517 Room: HB-06.040 E-mail: f(dot)h(dot)vandermeulen(at)tudelft(dot)nl |
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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.
F.H. van der Meulen (2022) arXiv This is an attempt to introduce some of
the ideas of the paper Automatic Backward Filtering
Forward Guiding for Markov processes and graphical
models, (written jointly with M. Schauer) in a friendly
way.
M.A. Corstanje, F.H. van der Meulen
and M. Schauer (2021) Conditioning continuous-time
Markov processes by guiding, arXiv submitted
J. Bierkens, S. Grazzi, F.H. van der
Meulen and M. Schauer (2021) Sticky PDMP samplers for sparse
and local inference problems, arXiv, under revision.
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.
Update May 1. I gave a talk on this topic
for the Laplace-demon seminar laplace
demon seminar talk The paper is
still under revision, comments are welcome.
Topic: statistical inference for
stochastic processes
A. Arnaudon, F.H. van der Meulen, M.R. Schauer and S. Sommer (2022) Diffusion bridges for stochastic Hamiltonian systems and Shape Evolutions, arXiv , SIAM Journal on Imaging Sciences (SIMS), 15(1), 293-323
M. Mider, M.R. Schauer and F.H. van der Meulen (2021) Continuous-discrete smoothing of diffusions, arXiv , Electronic Journal of Statistics 15, 4295-4342 Good starting point if you are interested in inference for partially observed diffusions using backward filtering forward guiding.
J. Bierkens, S. Grazzi, F.H. van der Meulen and M. Schauer (2021) A piecewise deterministic Monte Carlo method for diffusion bridges, arXiv, Statistics and Computing 31(3)
J. Bierkens, F.H. van der Meulen and M. Schauer (2020) Simulation of elliptic and hypo-elliptic conditional diffusions. Advances in Applied Probability. 52, 173–212.
F.H. van der Meulen, M. Schauer, S.
Grazzi, S. Danisch and M. Mider (2020) Bayesian
inference for SDE models: a case study for an excitable
stochastic-dynamical model, Nextjournal, https://nextjournal.com/Lobatto/FitzHugh-Nagumo
S. Gugushvili, F.H. van der Meulen,
M. Schauer and P. Spreij (2020) Non-parametric Bayesian
estimation of a Holder continuous diffusion coefficient
Brazilian Journal of Probability and Statistics 34(3),
537-579. (pdf)
S. Gugushvili, F.H. van der Meulen and P.J. Spreij (2018) A non-parametric Bayesian approach to decompounding from high frequency data. Statistical Inference for Stochastic Processes, 21, 53-79.
S.
Gugushvili, F.H. van der Meulen, M. 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
F.H. van der Meulen and M. Schauer
(2017) Bayesian
estimation of incompletely observed diffusions, Stochastics
90(5), 641-662.
F.H. van der Meulen and M. Schauer
(2017) Bayesian
estimation of discretely observed multi-dimensional
diffusion processes using guided proposals,
Electronic Journal of Statistics 11(1), 2358--2396.
M. 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
F.H. van der Meulen, M. 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.
F.H. van der Meulen,
M. Schauer and J.H. van Zanten (2014) Reversible
jump MCMC for nonparametric drift estimation for diffusion
processes, Computational Statistics and Data
Analysis 71, 615--632.
S.
Gugushvili, S., P. Spreij and F.H. van
der Meulen (2015) Non-parametric
Bayesian inference for multi-dimensional compound Poisson
processes. Modern Stochastics: Theory and
Applications 2(1), 1--15.
F.H. van der Meulen
and J.H. van Zanten (2013) Consistent
nonparametric Bayesian inference for discretely observed
scalar diffusions, Bernoulli 19(1),
44–63.
F.H. van der Meulen, A.W. van der Vaart and J.H. van Zanten (2006) Convergence rates of posterior distributions for Brownian semimartingale models Bernoulli 12(5), 863-888
G. Jongbloed and F.H. van der Meulen
(2006) Parametric
estimation for subordinators and induced OU-processes Scandinavian Journal of Statistics 33(4),
825-847
G. Jongbloed, F.H. van der Meulen and A.W. van der Vaart (2005) Nonparametric inference for Lévy driven Ornstein-Uhlenbeck processes. Bernoulli 11(5), 759-791
F.H. van der Meulen (2005)
Statistical
estimation for Levy driven OU-processes and Brownian
semimartingales , Phd-thesis, Vrije Universiteit
Amsterdam.
M.B. Vermaat, F.H. van der Meulen and R.J.M.M. Does (2008) Asymptotic Behaviour of the Variance of the EWMA Statistic for Autoregressive Processes Statistics and Probability Letters 78(12), 1673-1682
H.J.J. Ramaker, E.N.M. van Sprang, J.A. Westerhuis, S.P. Gorden, F.H. van der Meulen and A.K. Smilde (2006) Performance assessment and improvement of control charts for statistical batch process monitoring Statistica Neerlandica 60(3), 339-360 2)2001-2005: 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.
Some of the packages I have written include
- BridgeLandmarks
(Julia-registrered package containing code for
stochastic deformation models using bridge simulation,
written with M. Schauer)
- BayesianDecreasingDensity
(Bayesian nonparametric estimation of a decreasing
density)
- Bdd
(Bayesian
decompounding of discrete distribution, written with S.
Gugushvili)
- PointProcessInference
(nonparametric estimation of the intensity of a
non-homogeneous Poisson process, written with S.
Gugushvili and M. Schauer)