Modularization of complex tasks

Project Details


Divide and conquer techniques are widespread in computer science, but have not been explored thoroughly in graphical models. A method for splitting up a Bayesian network into components that can be treated independently with respect to many of the common operations has been constructed . This method forms the foundation for various tasks such as distributed triangulation and hybrid inference. Special emphasis has been directed towards the development of algorithms for incremental compilation.
Effective start/end date19/05/201031/12/2015


  • <ingen navn>


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.