Professor in Mathematical Statistics
Vrije Universiteit Amsterdam
Department of Mathematics
De Boelelaan 1111, 1081 HV Amsterdam, Netherlands
Room: NU-9A-67
Email: f(dot)h(dot)van(dot)der(dot)meulen(at)vu(dot)nl
Research Summary
My research is directed to statistical inference for stochastic
processes, with focus on uncertainty
quantification and indirect
observation schemes. I work on Bayesian
computational aspects of inference for discretely observed
stochastic processes on graphical models with particular
emphasis on diffusion processes. I have developed general
methods for simulating conditioned Markov processes using an
algorithm called backward filtering forward guiding.
Applications include stochatic shape deformation, inference
methods for diffusions on manifolds, chemical reaction
networks and filtering methods for partically observed
processes defined in terms of a stochastic partial
differential equation.
More generally, I am interested in Bayesian computational
methods such as Markov Chain Monte Carlo (MCMC) and Sequential
Monte Carlo (SMC). I have worked on piecewise deterministic
processes such as the (sticky) ZigZag process. On a somewhat
more theoretical level I am interested in proving posterior
contraction rates for Bayesian procedures, for example in
censoring or shape restricted nonparametric problems.
Finally an important aspect of my research consists of direct
collaboration with researchers in fields outside mathematics.
Examples include sports engineering, climate projections
and maritime engineering (fatigue calculations of maritime
structures).
Some research keywords:
statistical inference for stochastic
processes; Bayesian computation; filtering; graphical
models; dynamical systems; shape analysis, stochastic
differential equations.
If we share research interests, feel free
to send me an email to discuss possibilities for
collaboration.
Organisation - services to the
community:
- Director of the VU bachelor programme
Business
Analytics
- Chair of the section Mathematical
Statistics of the Netherlands Society for Statistics and
Operations Research (VVSOR)
- Organiser of the nationwide seminar in
statistics (Van
Dantzig seminar)
- Member of the STAR (Stochastics
Applied Research) - board.
Oratie (inaugural speech):
(January 24 2025): my inaugural address was published in Nieuw
Archief voor de Wiskunde (inaugural).
These are the presentation slides and this is one animation
used.
Consulting Requests
I offer consulting services in statistics, data analytics,
machine learning, and related areas. More info here.
Preprints
- F.H. van der Meulen, M. Schauer
and Andi Q. Wang (2025) Compositionality in
algorithms for smoothing arXiv
Submitted.
- F.H. van der Meulen, M. Schauer
and S. Sommer (2025) Backward Filtering Forward
Guiding. arXiv
Submitted. I gave a
talk on a preliminary version of this paper at the
Laplace-demon seminar laplace
demon seminar talk.
- S. Stroustrup, M. Akhoej Pedersen, F. van der Meulen, S.
Sommer and R. Nielsen (2025) Stochastic
Phylogenetic Models of Shape BioRXiv
Submitted.
- T.
Pieper-Sethmacher, F.H. van der Meulen and
A.W. van der Vaart (2025) Simulation
of infinite-dimensional diffusion
bridges arXiv
- F.H.
van der Meulen, L. van der Graaff and E.
Verhagen (2025) Monitoring athlete
health through latent state modelling
- F.H. van der Meulen (2022) Introduction to Automatic
Backward Filtering Forward Guiding (v2, Nov2022)
arXiv (This is an informal introduction to BFFG)
Publications
Full
list of publications (last updated 28-06-2025)
Recent publications
- Marco Reijne, Frank van der Meulen,
Frans van der Helm and Arend Schwab (2025) A model
on cyclist fall experiments which predicts the
maximum allowable handlebar disturbance from which a
cyclist can recover balance. Accepted for
publication in Accident Analysis and
Prevention.Marc
Corstanje,
- Frank van der Meulen, Moritz Schauer and Stefan Sommer
(2025) Simulating conditioned diffusions on manifolds.
arXiv
Accepted for publication in Bernoulli.
- Gefan Yang, Frank van der Meulen and
Stefan Sommer (2025) Neural guided diffusion
bridges.arXiv
Accepted for ICML 2025.
- M.A. Corstanje and F.H. van der Meulen (2025) Guided
simulation of conditioned chemical reaction networks.
arXiv
Accepted for publication in Statistical Inference for
Stochastic Processes. link
to SISP
- T. Pieper-Sethmacher, F.H. van der Meulen and A.W. van der
Vaart (2025) On a class of exponential changes of
measure for stochastic PDEs arXiv
Stochastic Processes and their Applications. link
to SPA
- Rikkert Hindriks, Frank van der Meulen,
Michel van Putten and Prejaas Tewarie (2025) Unraveling
high-order interactions in electrophysiological
brain signals using elliptical distributions: Moving
beyond the Gaussian approximation. Accepted for
publication in Journal of Physics: Complexity. Link
to journal
Some older publications
- J. Bierkens, S. Grazzi, F.H. van der Meulen and M. Schauer
(2023) Sticky PDMP samplers for sparse and local
inference problems, arXiv, Statistics
and Computing 33(8).
- 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
- M. Schauer, F.H. van der Meulen and
H. van Zantenj (2017). Guided proposals for simulating
multi-dimensional diffusion birdges. Bernoulli 23(4A),
2917--2950
- G. Jongbloed, G. and F.H. van der Meulen
(2009) Estimating a concave distribution
function from data corrupted with additive noise.
Annals of Statistics 37(2), 782-815.
- 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.
Some "outreach" publications
- F.H. van der Meulen (2025) Statistiek:
het onzichtbare zichtbaar maken. Nieuw Archief
voor Wiskunde 26(2), 109--117.
- A. di Bucchianico, L. Iapichino, N. Litvak, F.H. van der
Meulen and R. Wehrens (2018) Mathematics for Big Data
Nieuw Archief voor wiskunde, 282-287. pdf reprinted in `The Best Writing on
Mathematics 2019' link
to book
Unpublished
- F.H. van der Meulen and M.R. Schauer (2017) On
residual and guided proposals for diffusion bridge
simulation arXiv
- F.H. van der Meulen and M. Schauer (2022) Automatic
Backward Filtering Forward Guiding for Markov processes
and graphical models, arXiv
Working paper that evolved into two manuscripts Backward
Filtering Forward Guiding (with Stefan Sommer) and Compositionality
in algorithms for smoothing
(with Andi Q. Wang).
Short CV
2001--2005: PhD student, VU Amsterdam
2005--2007: Researcher at IBIS UvA
2007--2017: Assistant professor, TU Delft
2018--2022: Associate professor, TU Delft
2022--now: Full professor, VU Amsterdam
Teaching
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: statistical
inference and time-series.
Software
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)
Presentations
- Likelihood representations for discretely observed
stochastic processes (Bergamo-Waseda Workshop on Inference
for Stochastic Processes and Applications, January 2023). slides
- Backward Filtering Forward Guiding (Warwick Algorithms and
Computationally Intensive Inference Seminar (ACIIS),
December 2022). slides
- Continuous-discrete smoothing of diffusions (Imperial
college, December 2017). slides
- Nonparametric Bayesian Decompounding (European Meeting of
Statisticians Amsterdam 2015). slides
- Convergence rates of posterior distributions for Brownian
semimartingale models (European Meeting of Statisticians
2005 Oslo). slides
Math Books
Here are some books in math I like:
- Probability 1 and 2 by Albert Shiryaev (just
wonderful how everything is set ready in the very first
chapter to do the more advanced stuff; I also very much like
the statistically oriented examples).
- Linear Algebra Done Right by Sheldon Axler (a
didactical masterpiece, not for a first introduction to
linear algebra).
- Introductory Functional Analysis with Applications
by Erwin Kreyszig (a classic).
- Vector Calculus, Linear Algebra and Differential forms
by John Hubbard and Barbara Burke Hubbard (I haven't seen
other books with such a unique combination of topics
explained well; btw, why doesn't this book ship for any
affordable price to The Netherlands?)
- Pattern Recognition and Machine Learning by
Christopher Bishop.
- Probably Theory by Edwin Jaynes (for anyone
something to disagree on in this book, but I learned a lot
from it and it surely influenced my point of view on
statistics).
- R for Data Science by Hadley Wickham and Garrett
Grolemund (I think the tidyverse packages are a great
service to practitioners).
- A Student's Guide to Bayesian Statistics by Ben
Lambert (many people that use statistics never learned math;
this book explains the Bayesian approach well, also
conceptually the more advanced topics).
- Asymptotic Statistics by Aad van der Vaart.