{"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/unbiased-bayesian-inference-for-population","title":"Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations","arxiv_id":"1509.08327","date":"2015-09-28","proceeding":null,"authors":["Anastasis Georgoulas","Jane Hillston","Guido Sanguinetti"],"abstract":"We consider continuous time Markovian processes where populations of\nindividual agents interact stochastically according to kinetic rules. Despite\nthe increasing prominence of such models in fields ranging from biology to\nsmart cities, Bayesian inference for such systems remains challenging, as these\nare continuous time, discrete state systems with potentially infinite\nstate-space. Here we propose a novel efficient algorithm for joint state /\nparameter posterior sampling in population Markov Jump processes. We introduce\na class of pseudo-marginal sampling algorithms based on a random truncation\nmethod which enables a principled treatment of infinite state spaces. Extensive\nevaluation on a number of benchmark models shows that this approach achieves\nconsiderable savings compared to state of the art methods, retaining accuracy\nand fast convergence. We also present results on a synthetic biology data set\nshowing the potential for practical usefulness of our work.","url_abs":"http://arxiv.org/abs/1509.08327v2","url_pdf":"http://arxiv.org/pdf/1509.08327v2.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":"unbiased-bayesian-inference-for-population","repo_url":"https://github.com/ageorgou/roulette","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian 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}