2017-05-19 Optimisation and Counting in Graphical Models
19 mai à 14h, salle des séminaires (bâtiment 4)
Thomas Schiex (DR à l'INRA-Toulouse)
"Graphical models" (GMs) define a family of mathematical modeling frameworks that have been independently explored as deterministic models (Constraint and Cost Function Networks - CN/CFNs) and stochastic models (eg. Markov Random Fields - MRFs, factor graphs). In this talk, I'd like to show that while dynamic and linear programming underlie both local consistency filtering in CN/CFNs and message passing in MRFs, the specific focus on optimized guaranteed methods for deterministic GMs translates into a technology that is directly useful for stochastic models. This is true both for decision NP-complete optimization (Maximum a Posteriori or MAP-MRF problem) and for #P-complete counting (computing Z, the normalizing constant aka the Partition Function). This will be illustrated on recent results obtained in Computational Structural Biology in the context of protein design.
Dernière mise à jour le 15/05/2017