Skip to yearly menu bar Skip to main content


Infinite Hidden Semi-Markov Modulated Interaction Point Process

matt zhang · Peng Lin · Peng Lin · Ting Guo · Yang Wang · Yang Wang · Fang Chen

Area 5+6+7+8 #163

Keywords: [ Time Series Analysis ] [ Graphical Models ] [ (Other) Bayesian Inference ] [ Stochastic Methods ] [ (Other) Applications ] [ Bayesian Nonparametrics ]


The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e.g., hidden Markov model) and stochastic interaction point process models (e.g., Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.

Live content is unavailable. Log in and register to view live content