Speaker
Guido Sanguinetti
(University of Edinburg)
Description
Stochastic processes are widely used mathematical models in
disciplines ranging from biology to physics and economics.
Consequently, there has been considerable interest in the
statistics and machine learning communities in devising
approximate Bayesian inference methods for specific classes
of stochastic processes. The general scenario considered is
that the data consists of noisy observations of the state of
the system at discrete time points. While this is clearly an
important scenario, I will argue that it is natural to also
consider another type of observations which globally
characterise trajectories of the system. These "phenotypic"
observations are naturally expressed as constraints which
must hold for a continuous subset of the observation
interval, i.e. they are "continuous time observations". I
will consider two approaches for learning in such systems: a
general purpose Gaussian Process optimisation method for
maximum likelihood parameter estimation, and a message
passing approximate inference algorithm for posterior
inference for diffusion processes.
