News & Events

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

User menu

You are here

Understanding Food Webs: Twitter, Critters, Bugs, Python, and More

Tuesday, August 19, 2014 - 11:00
Grace Chiu Ph.D., Senior Research Scientist, Digital Productivity and Services Flagship (formerly Mathematics, Informatics and Statistics), Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia
Statistics Seminar
Room 4192, Earth Sciences Building (2207 Main Mall)

Any network or web-like structure consists of "actors" that are linked by some type of activity.  Thus, inference for features in a social network or a predator-prey food web can be made using a common statistical framework as foundation. Our Bayesian methodology extends upon latent space network modelling to address the notion of "trophic levels" from three perspectives of feeding behaviour: (1) activity level as predator and prey, (2) feeding preference as predator, and (3) feeding preference as prey.  We present worked examples, and discuss the implementation of MCMC using brute force, (Open/Win)BUGS, and (Py)Stan.