The goal of this project is to develop and analyze statistical inference methods for networks driven by stochastic processes. A complication that one typically faces is that the stochastic process feeding the network is not observed directly, and therefore indirect estimation techniques are needed. In a quintessential example, from snapshots of the network population one wishes to estimate the network's input characteristics. While for specific models results have been established, this area still offers a broad array of challenging open questions. The project lies at the intersection of applied probability and mathematical statistics. Potential application areas include service systems and communication networks.
|Supervisors||Michel Mandjes (UvA), Liron Ravner (UHaifa)|
|PhD Student||Boris Lebedenko (UvA)|
|Location||University of Amsterdam (UvA)|