Inference and Learning with Graphical Models
Intelligent systems which work in the real world cooperating with humans should be able to treat complex, uncertain, dynamic, multi-modal information in the real world environment. Probability distributions with graphical structure (Graphical Models) are recently considered as a promising fundamental tools to represent and process such information. From this point of view, toward a theoretical and algorithmic foundation for real world intelligence, we are investigating inference and learning algorithms for several kinds of graphical models such as Bayesian networks, probabilistic constraint program, mixture models, multivariate models. Novel learning frameworks suited for real world intelligence are also investigated. Here we introduce some remarkable results from our recent researches.