International Society for Bayesian Analysis

This course will advocate a Bayesian approach to modelling and inference
for dynamic stochastic models of biological systems. An introduction will
be given to the theory of Markov processes in continuous time, and their
application to biological modelling. Discrete state jump processes and
associated diffusion approximations described by stochastic differential
equations will be discussed, with a strong emphasis on algorithms and
computer simulation.

Also, Bayesian inference for hierarchical dynamic state-space models will
be considered, together with Bayesian inference for stochastic differential
equations. Particular emphasis will be placed on "likelihood free" methods
of inference, including approximate Bayesian computation and particle MCMC
algorithms.

The course will emphasise model development, simulation and computationally
intensive methods of inference based on realistic examples.

The school will make use of lectures, practical sessions, software
demonstrations, informal discussion sessions and presentations of research
projects by school participants. The slides and background reading material
will be distributed to the students before the start of the course.

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Guido Consonni and Fabrizio Ruggeri
ABS12 Directors

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