2015/11/30 by Trevor Fenner, Mark Levene, George Loizou · 6 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Computer science #Exponential distribution #Exponential function #Human dynamics #Mathematics #Mixture model #Opinion Dynamics and Social Influence #Reliability (semiconductor) #Set (abstract data type) #Simple (philosophy) #Statistics #Stochastic modelling #cs.SI #physics.soc-ph
paper · pdf · doi:10.1140/epjb/e2016-60926-8
published in The European Physical Journal B 89(2) (Springer Science+Business Media) · 14 pages. arXiv admin note: substantial text overlap with arXiv:1502.07558
arxiv created 2016/01/14 · openalex publication_date 2016/02/01 · arxiv updated 2016/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in \citeFENN15 so that it can generate mixture models,in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.