STATISTICAL METHODS FOR DYNAMICAL STOCHASTIC MODELS
In the period 2000 - 2004, Dynstoch was a research
training network under the programme Improving Human
Potential financed by the The Fifth Framework Programme
of the European Commission. The research
activities continue, and the network has been continued and expanded
without EU funding. The web-site and the newsletter continue,
and every year an annual workshop is held. Keep yourself informed
about the activities by joining the DYNSTOCH
The Annual DYNSTOCH Workshop:
The DYNSTOCH workshop in 2023 will take place at
Imperial College London 27 - 29 March
Venue: 170 Queen's Gate at Imperial College's South
Organizers: Dan Crisan and Almut Veraart
For more information, please go to the
Deadline for abstract submission: 9 January 2023
Deadline for registration: 6 February 2023
The list includes the workshops of the previous EU research network,
Statistical Inference for Stochastic processes, out of which the
Dynstoch network grew.center>
The DYNSTOCH mailing list and
You can be kept informed
about the activities of the network by joining the DYNSTOCH mailing list.
The subscribers receive the DYNSTOCH Newsletter from time to time.
To subscribe or unsubscribe to the mailing list, please go to https://lists.ku.dk/postorius/lists/sci-math-dynstoch.list.ku.dk/
and follow the instructions.
In many fields complex dynamical stochastic models are needed to describe
that develop in time and/or space in a random way, usually with temporal
or spatial interactions that are important for a proper understanding of the
phenomenon under study and for making predictions about the system.
A few concrete examples of such stochastic processes are: Interest rates,
turbulent flows, communication in networks of neurons, and protein
The high speed of present day computers has made use of complex stochastic
models feasible, and at the same time, the important developments that have
taken place in probability theory, in particular in the area of stochastic
calculus, have only to a limited extent been used by statisticians to
develop statistical methods for stochastic processes.
The principal aim of the DYNSTOCH network is to make a major contribution
to the statistical theory and methodology for stochastic processes
by taking advantage of the tools of modern probability theory including
stochastic calculus and by using highly computer-intensive methods. This
is partly done by modelling and statistical data analysis in a number of
subject areas including neuro science, physiology, biology, turbulence
(wind energy) and finance.
The Original DYNSTOCH Teams:
The original DYNSTOCH network had the following 9 teams. The names in the
parentheses are the scientists in charge of the teams.
Copenhagen Team (Michael Sørensen),
Amsterdam Team (Peter Spreij),
Berlin Team (Uwe Küchler),
Cartagena Team (Mathieu Kessler),
Freiburg Team (Ernst Eberlein),
Helsinki Team (Esko Valkeila, deceased),
London Team (Valerie Isham),
Padua Team (Andrea Gombani), and
Paris Team (Jean Jacod).
Here is a list of the young researchers who
were employed under the Dynstoch contract in 2000 - 2004.
This page was last updated on August 2, 2022.