Bayesian parameter inference in discrete state-space stochastic population models
- Event time: 11:30am
- Event date: 8th June 2011
- Speaker: Glenn Marion (University of Edinburgh)
- Location: Room 2511, James Clerk Maxwell Building (JCMB) James Clerk Maxwell Building Peter Guthrie Tait Road Edinburgh EH9 3FD GB
Continuous-time discrete state-space Markov processes are useful dynamic models for a wide range of systems in biology. These include wildlife populations, chemical kinetics (in small volumes), animal behaviour, and the spread of disease in space and time. In some cases model parameters can be determined from targeted experiments, but in many cases appropriate data is not available, or model parameters can't be determined independently. It is then desirable to determine combined parameter values from direct/global observations of the system. In this talk I will outline a Bayesian approach to parameter inference from incomplete data as applied to such models and systems. I will attempt to provide a genuine introduction to the topic: (i) outlining the Bayesian approach and why it is a natural choice for such problems; (ii) describing the mechanics of applying the approach; and (iii) describing some examples and associated practical problems.
This is a roughly weekly series of didactical blackboard talks focussing on some theoretical aspect of Condensed Matter, Biological, and Statistical Physics..