Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This addresses the problem of scalable structured point process modeling for researchers and practitioners dealing with dependent event data.
The paper tackles modeling dependent Cox point processes by introducing a Bayesian generative model with an efficient inference scheme that scales as if processes were independent, achieving vastly improved predictive performance on 1D and 2D real data.
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.