{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mutually-regressive-point-processes","title":"Mutually Regressive Point Processes","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Ifigeneia Apostolopoulou","Scott Linderman","Kyle Miller","Artur Dubrawski"],"abstract":"Many real-world data represent sequences of interdependent events unfolding over\n time. They can be modeled naturally as realizations of a point process. Despite many potential applications, existing point process models are limited in their\nability to capture complex patterns of interaction. Hawkes processes admit many\nefficient inference algorithms, but are limited to mutually excitatory effects. Non-\nlinear Hawkes processes allow for more complex influence patterns, but for their\nestimation it is typically necessary to resort to discrete-time approximations that may yield poor generative models. In this paper, we introduce the first general\nclass of Bayesian point process models extended with a nonlinear component that\nallows both excitatory and inhibitory relationships in continuous time. We derive a fully Bayesian inference algorithm for these processes using Polya-Gamma augmentation and Poisson thinning. We evaluate the proposed model on single\nand multi-neuronal spike train recordings. Results demonstrate that the proposed\nmodel, unlike existing point process models, can generate biologically-plausible\nspike trains, while still achieving competitive predictive likelihoods.","url_abs":"http://papers.nips.cc/paper/8755-mutually-regressive-point-processes","url_pdf":"http://papers.nips.cc/paper/8755-mutually-regressive-point-processes.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mutually-regressive-point-processes","repo_url":"https://github.com/ifiaposto/Mutually-Regressive-Point-Processes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[{"method_slug":"polya-gamma-augmentation","method_name":"Polya-Gamma Augmentation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}