OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
With complete data and appropriately chosen parameter priors the problem of finding a Bayesian network with maximal log marginal likelihood (LML) becomes a purely discrete problem: search for a directed acyclic graph (DAG) with maximal LML. We solve this problem of discrete optimisation using integer linear programming (ILP) with the SCIP (Solving Constraint Integer Programming) framework. In many cases this allows us to solve the problem: we find a DAG which we know to have maximal LML. Also using ILP allows prior knowledge, such as known conditional independence relations, to be expressed as constraints on DAG structure The key to efficient solving is to add certain linear constraints ruling out cyclic digraphs during the search. I will report on the successes and limitations of this approach and discuss future directions.
