{"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/bayesian-nonparametric-poisson-process","title":"Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling","arxiv_id":"1705.07006","date":"2017-05-19","proceeding":null,"authors":["Hongyi Ding","Mohammad Emtiyaz Khan","Issei Sato","Masashi Sugiyama"],"abstract":"Analyzing the underlying structure of multiple time-sequences provides\ninsights into the understanding of social networks and human activities. In\nthis work, we present the \\emph{Bayesian nonparametric Poisson process\nallocation} (BaNPPA), a latent-function model for time-sequences, which\nautomatically infers the number of latent functions. We model the intensity of\neach sequence as an infinite mixture of latent functions, each of which is\nobtained using a function drawn from a Gaussian process. We show that a\ntechnical challenge for the inference of such mixture models is the\nunidentifiability of the weights of the latent functions. We propose to cope\nwith the issue by regulating the volume of each latent function within a\nvariational inference algorithm. Our algorithm is computationally efficient and\nscales well to large data sets. We demonstrate the usefulness of our proposed\nmodel through experiments on both synthetic and real-world data sets.","url_abs":"http://arxiv.org/abs/1705.07006v5","url_pdf":"http://arxiv.org/pdf/1705.07006v5.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":"bayesian-nonparametric-poisson-process","repo_url":"https://github.com/Dinghy/BaNPPA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}