Professor in Mathematical
Statistics Vrije Universiteit Amsterdam Department of Mathematics VU_math De Boelelaan 1111, 1081HV Amsterdam The Netherlands Room: NU9A35 Email: f(dot)h(dot)van(dot)der(dot)meulen(at)vu(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.
Topic: statistical inference for
stochastic processes
Denis Belomestny, Frank van der
Meulen, and Peter Spreij (2023), Nonparametric Bayesian
inference for stochastic processes with piecewise constant
priors, Mathematics of Risk 2022 MATRIX Annals, Editors:
David R. Wood, Jan de Gier, Cheryl E. Praeger, Terence Tao.
MATRIX Book Series, Springer, to appear. [pdf available at the 202122
MATRIX Annals page]
This is
an overview paper, showing a common theme in
various earlier applications, also jointly with S.
Gugushvili and M. Schauer.
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).
This
paper shows how to adapt piecewise deterministic Markov
processes to settings where the dominating measure is not
Lebesgue measure. As an example, it shows how to sample from
the posterior under a spikeandslab prior, without the need
to set any additional tuning parameter.
M.A. Corstanje, F.H. van der Meulen and M. Schauer (2023) Conditioning continuoustime Markov processes by guiding, Stochastics, 95(6), 963996.
M. Mider, M.R. Schauer and F.H. van
der Meulen (2021) Continuousdiscrete smoothing of
diffusions, arXiv ,
Electronic Journal of Statistics 15, 42954342
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 hypoelliptic 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
stochasticdynamical model, Nextjournal, https://nextjournal.com/Lobatto/FitzHughNagumo
S. Gugushvili, F.H. van der Meulen,
M. Schauer and P. Spreij (2020) Nonparametric Bayesian
estimation of a Holder continuous diffusion coefficient Brazilian
Journal of Probability and Statistics 34(3), 537579.
(pdf)
S. Gugushvili, F.H. van der Meulen and P.J. Spreij (2018) A nonparametric Bayesian approach to decompounding from high frequency data. Statistical Inference for Stochastic Processes, 21, 5379.
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), 641662.
F.H. van der Meulen and M. Schauer
(2017) Bayesian
estimation of discretely observed multidimensional
diffusion processes using guided proposals,
Electronic Journal of Statistics 11(1), 23582396.
M. Schauer, F.H. van
der Meulen and J.H. van Zanten (2017) Guided
proposals for simulating multidimensional diffusion bridges,
Bernoulli 23(4A), 29172950
F.H. van der Meulen, M. Schauer, J. van Waaij
(2017) Adaptive
nonparametric drift estimation for diffusion processes
using FaberSchauder expansions,
Statistical Inference for Stochastic Processes 21(3),
603628.
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, 615632.
S.
Gugushvili, S., P. Spreij and F.H. van
der Meulen (2015) Nonparametric
Bayesian inference for multidimensional compound Poisson
processes. Modern Stochastics: Theory and
Applications 2(1), 115.
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), 863888
G. Jongbloed and F.H. van der Meulen
(2006) Parametric
estimation for subordinators and induced OUprocesses Scandinavian Journal of Statistics 33(4),
825847
G. Jongbloed, F.H. van der Meulen and A.W. van der Vaart (2005) Nonparametric inference for Lévy driven OrnsteinUhlenbeck processes. Bernoulli 11(5), 759791
F.H. van der Meulen (2005)
Statistical
estimation for Levy driven OUprocesses and Brownian
semimartingales , Phdthesis, 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), 16731682
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), 339360 2)20012005: PhD student
at Vrije Universiteit Amsterdam
20052007: Consultant/researcher at the Institute
for Business and Industrial Statistics at the University
of Amsterdam (IBIS UvA)
20072017: Assistant professor at TU Delft
20182022: Associate professor at TU Delft
2022now: Full professor at Vrije Universiteit
Amterdam
2012now: 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: statistical
inference and timeseries.
I enjoy implementing new
computational ideas, see my Github account.
Some of the packages I have written include
 BridgeLandmarks
(Juliaregistrered 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
nonhomogeneous Poisson process, written with S.
Gugushvili and M. Schauer)
 Likelihood representations
for discretely observed stochastic processes
(BergamoWaseda 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
 Continuousdiscrete 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
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.