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
Some keywords: statistical
inference for stochastic processes (diffusions, Lévy
processes); Bayesian computation; Bayesian asymptotics;
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:
I offer consulting services in statistics, data analytics, machine learning, and related areas. More info here.
Two papers that are related to smoothing and parameter
estimation for stochastic processes evolving on graphical
models:
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). I gave a talk on this topic for the Laplace-demon seminar laplace demon seminar talk. The categorical part has evolved into the manuscript "Compositionality in algorithms for smoothing". The statistical part will evolve into a separate manuscript.
Topic: statistical inference for stochastic processes
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
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.
I enjoy implementing new computational ideas, see my Github account. Some
of the packages I have written include