Statistical inference of causal and ordinary differential equation models

Networks arise from modeling complex systems in various aspects in the science. Analysing the network structure help us better understand these complex systems and extract useful information.
One important problem in network analysis is to model the underlying generating mechanism of networks based on data structures and then establish the nature of the dependence. Inferring causal relationship among the nodes from observational sample data or a mixture of observational sample and experimental data, particularly in the area of graphical causal modelling, is challenging.
For instance, understanding the structure of biological networks and elucidating networks of gene interactions underlying complex human phenotypes represents a major challenge in systems biology.
One of the interesting subjects after constructing the network is detecting the dynamics of the network. Ordinary differential equations provide an attractive class of models for the dynamics of these networks. In this thesis we contributes methodology for improve the estimation of causal networks and dynamical systems.