6 papers
Bayesian Modelling of Nonstationary Extreme Values Using a Nonparametric Hawkes Process
Gordon J. Ross, Dean Markwick
Modelling and forecasting the occurrence of extreme events is especially difficult when the event process is nonstationary, with changes in both the rate at which extremes occur an…
Sequential Bayesian Monitoring for Recoverable and Drifting Processes
Gordon J. Ross
In many Phase II statistical process control (SPC) problems, the main concern is not whether a monitored process has ever changed, but whether it is currently operating at an accep…
The Ancestor Hawkes Process with an Application to Group Chat Data
Gordon J Ross, Isabella Deutsch
The Hawkes process is used to model point process data where events occur in clusters and bursts. In a standard multivariate Hawkes process, every event that occurs in a dimension…
dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
Gordon J. Ross, Dean Markwick, Priyanshu Tiwari
The dirichletprocess package provides software for creating flexible Dirichlet process objects. Users can perform nonparametric Bayesian analysis using Dirichlet processes without…
Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation
Gordon J. Ross
Change point methods are used to divide a sequence of observations into segments with different behaviour. Often, the distributional form of the observations is unknown, but the ch…
A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Trinnhallen Brisley, Gordon Ross, Daniel Paulin
Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure. Traditional Hawkes process…