Bayesian model determination in complex systems

Abdolreza Mohammadi

Research output: ThesisThesis fully internal (DIV)

1393 Downloads (Pure)


One of the main challenges in modern science is the accurate and complete description of complex systems. The difficulty of modeling complex systems lies partly in their topology and how they form rather complex networks. From this perspective, our interest to modeling networks is part of a broader movement towards research on complex systems.

In this thesis, we develop several statistical methods that jointly model the underlying network and its structure among variables in the system. These methods are easy to implement and computationally feasible for complex networks. We cover the theory and computational details of the methods. We have extended our statistical method such that it works for different types of complex data. We applied it to a Dupuytren disease dataset to discover potential risk factors. We have implemented our methods as a user-friendly statistical software called BDgraph, which is freely available online.
Translated title of the contributionBayesiaanse modelbepaling in complexe systemen
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Groningen
  • Wit, Ernst, Supervisor
Award date24-Apr-2015
Place of Publication[Groningen]
Print ISBNs978-90-367-7831-2
Electronic ISBNs978-90-367-7832-9
Publication statusPublished - 2015

Cite this