{"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/determinantal-thinning-of-point-processes","title":"Determinantal thinning of point processes with network learning applications","arxiv_id":"1810.08672","date":"2018-10-09","proceeding":null,"authors":["Bartłomiej Błaszczyszyn","Paul Keeler"],"abstract":"A new type of dependent thinning for point processes in continuous space is\nproposed, which leverages the advantages of determinantal point processes\ndefined on finite spaces and, as such, is particularly amenable to statistical,\nnumerical, and simulation techniques. It gives a new point process that can\nserve as a network model exhibiting repulsion. The properties and functions of\nthe new point process, such as moment measures, the Laplace functional, the\nvoid probabilities, as well as conditional (Palm) characteristics can be\nestimated accurately by simulating the underlying (non-thinned) point process,\nwhich can be taken, for example, to be Poisson. This is in contrast (and\npreference to) finite Gibbs point processes, which, instead of thinning,\nrequire weighting the Poisson realizations, involving usually intractable\nnormalizing constants. Models based on determinantal point processes are also\nwell suited for statistical (supervised) learning techniques, allowing the\nmodels to be fitted to observed network patterns with some particular geometric\nproperties. We illustrate this approach by imitating with determinantal\nthinning the well-known Mat{\\'e}rn~II hard-core thinning, as well as a\nsoft-core thinning depending on nearest-neighbour triangles. These two examples\ndemonstrate how the proposed approach can lead to new, statistically optimized,\nprobabilistic transmission scheduling schemes.","url_abs":"http://arxiv.org/abs/1810.08672v2","url_pdf":"http://arxiv.org/pdf/1810.08672v2.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":"determinantal-thinning-of-point-processes","repo_url":"https://github.com/hpaulkeeler/DetPoisson_MATLAB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"},{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}