5 accepted papers
The Hawkes process has been applied successfully to model point process data in a number of application areas including seismology, neural coding, high-frequency finance, and genomics. But the Hawkes process does not accommodate inhibitory effects observed jointly with excitatory behavior, e.g., for…
In a range of network system identification problems point processes are the nodal signals. In many of these applications a fundamental question involves characterizing the internodal dynamics. Here we address that question using mutual information flows and establish a connection with Granger causa…
Increasingly in the `big data' era, point process network data is appearing e.g. in social media but also genomics, high frequency finance and neurosicence. And the most basic problem is that of identifying network structure. As with all network system identification problems this is enabled by the…
The Hawkes process is the workhorse of dynamic point process modelling - the point process version of the autoregression. It has been applied, for example, in high frequency finance, electricity price spike modelling and gene regulatory network modelling. But, in all these and other applications, it…
Recently, the availability of high-dimensional point process data in a growing number of application areas is driving the demand for analysis tools for such data. An extremely challenging yet important problem is the inference of causal relationships from network data or network topology identificat…