{"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/scalable-bayesian-inference-for-excitatory","title":"Scalable Bayesian Inference for Excitatory Point Process Networks","arxiv_id":"1507.03228","date":"2015-07-12","proceeding":null,"authors":["Scott W. Linderman","Ryan P. Adams"],"abstract":"Networks capture our intuition about relationships in the world. They\ndescribe the friendships between Facebook users, interactions in financial\nmarkets, and synapses connecting neurons in the brain. These networks are\nrichly structured with cliques of friends, sectors of stocks, and a smorgasbord\nof cell types that govern how neurons connect. Some networks, like social\nnetwork friendships, can be directly observed, but in many cases we only have\nan indirect view of the network through the actions of its constituents and an\nunderstanding of how the network mediates that activity. In this work, we focus\non the problem of latent network discovery in the case where the observable\nactivity takes the form of a mutually-excitatory point process known as a\nHawkes process. We build on previous work that has taken a Bayesian approach to\nthis problem, specifying prior distributions over the latent network structure\nand a likelihood of observed activity given this network. We extend this work\nby proposing a discrete-time formulation and developing a computationally\nefficient stochastic variational inference (SVI) algorithm that allows us to\nscale the approach to long sequences of observations. We demonstrate our\nalgorithm on the calcium imaging data used in the Chalearn neural connectomics\nchallenge.","url_abs":"http://arxiv.org/abs/1507.03228v1","url_pdf":"http://arxiv.org/pdf/1507.03228v1.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":"scalable-bayesian-inference-for-excitatory","repo_url":"https://github.com/slinderman/pyhawkes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1507.03228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1507.03228"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/slinderman/pyhawkes","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f846a7adecd690f9","entry":"approximateFiringRate","repo":"slinderman/pyhawkes","repo_kind":"official","path":"pyhawkes/utils/poisson_process.py","file_url":"https://github.com/slinderman/pyhawkes/blob/HEAD/pyhawkes/utils/poisson_process.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f846a7adecd690f9"}},{"code_sha256_prefix":"ad02d994002cf065","entry":"sampleInhomogeneousPoissonProc","repo":"slinderman/pyhawkes","repo_kind":"official","path":"pyhawkes/utils/poisson_process.py","file_url":"https://github.com/slinderman/pyhawkes/blob/HEAD/pyhawkes/utils/poisson_process.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ad02d994002cf065"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}