Network models

Friday, October 26, 2012 - 10:15am - 11:05am
Lee DeVille (University of Illinois at Urbana-Champaign)
Dynamical systems defined on networks have applications
in many fields, including computational and theoretical neuroscience. In
particular, it is important to understand when networks exhibit synchronous or
other types of coherent collective behaviors. Other questions include whether
such coherent behavior is stable with respect to random perturbation, or what
the detailed structure of this behavior is as it evolves. We will examine several
models of networked dynamical systems and present a mixture of results that range
Tuesday, May 13, 2008 - 1:45pm - 2:45pm
Darren Wilkinson (University of Newcastle upon Tyne)
This talk will provide an overview of computationally intensive
methods for conducting Bayesian inference for the rate constants of
stochastic kinetic intracellular reaction network models using
single-cell time course data. Inference for the true Markov jump
process is extremely challenging in realistic scenarios, so the true
model will be replaced by a diffusion approximation, known in this
context as the Chemical Langevin Equation (CLE). Inference for the CLE
is also challenging, but the development of effective algorithms is
Tuesday, November 8, 2005 - 9:30am - 10:30am
Mark Coates (McGill University)
Many anomalous network events do not manifest themselves as abrupt,
easily-detectable changes in the volume of traffic at a single switch.
Rather, the footprint they leave is a modification of the pattern of traffic
at a number of routers in this network. Anomaly detection is then a question
of whether the current traffic pattern is sufficiently divergent from
normal traffic patterns. In this talk, I will describe a technique for
sequentially constructing a sparse kernel dictionary that forms a map of
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