MTO Colloquium Tuesday, March 8 WZ 104 at 12:45h By dr. Reza Mohammadi (Department Methodology & Statistics, Tilburg University) ‘Bayesian Structure Learning in Graphical Models’ Discovering complicated patterns among large number of variables is one of the main challenges in wide variety of applications ranging from social science to biology. The challenging question is how to extract the underlying network structure among those variables from information in data. To dealing with this issue, graphical models provide powerful tools and for the purpose of performing structure learning, Bayesian approaches provide a straightforward tool, explicitly incorporating underlying graph uncertainty. However, Bayesian inference in graphical models suffered from computational problems. In this regard, the key is to design computationally efficient search algorithms that are able to quickly move towards high posterior probability regions. This talk first will review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to deal with highdimensionally problems. We next introduce some computationally efficient search algorithms based on our Bayesian framework to extract underlying graph structure. The methods have been implemented in C++ and interfaced with R as an R package BDgraph which is freely available online. In addition, possible extensions of the methods and their applications in social science are discussed.