Speaker
Manfred Opper
(TU Berlin)
Description
Continuous time Markov processes (such as jump processes
and diffusions)
play an important role in the modelling of dynamical
systems in many
scientific areas ranging from physics to systems biology.
In a variety of applications, the stochastic state of the
system as a function of time is not
directly observed.
One has only access to a set of nolsy observations taken at
discrete
times.
The problem is then to infer the unknown state path as
best as possible.
In addition, model parameters (like diffusion constants or
transition rates) may also be
unknown and have to be estimated from the data.
Since Monte Carlo sampling approaches can be time
consuming
one is interested in efficient approximations.
I will discuss variational approaches to this problem which
are based
on methods developed in statistical physics and machine
learning and
which have also interesting relations to stochastic optimal
control.
Applications to transcriptional regulation in systems biology
will
be given.
