Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is ...

In this thesis project I analyse labour flow networks and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree ...

In this thesis we develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this ...

Hierarchical modeling is a practical approach with proven results in modeling real world data. This paper studies Gaussian hierarchical models and methods which exploit the sparse conditional independence structure of such ...

This paper studies a novel particle filter method proposed by Brownlees and Kristensen (2017) for parameter estimation of nonlinear state space models. The particle filter, named Importance Sampling Particle Filter, is tested ...