Traditionally, computational complexity has considered only static problems. Classical Complexity Classes such as NC, P, and NP are de ned in terms of the complexity of checking {...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Bargaining networks model the behavior of a set of players who need to reach pairwise agreements for making profits. Nash bargaining solutions in this context correspond to soluti...
Yashodhan Kanoria, Mohsen Bayati, Christian Borgs,...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Dynamic networks are those where the topology changes over time and therefore efficient routes need to be maintained by frequent updates. Such updates could be costly in terms of ...
Dominique Tschopp, Suhas N. Diggavi, Matthias Gros...