{"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/debinfer-bayesian-inference-for-dynamical","title":"deBInfer: Bayesian inference for dynamical models of biological systems in R","arxiv_id":"1605.00021","date":"2016-10-15","proceeding":null,"authors":[],"abstract":"1. Understanding the mechanisms underlying biological systems, and\nultimately, predicting their behaviours in a changing environment requires\novercoming the gap between mathematical models and experimental or\nobservational data. Differential equations (DEs) are commonly used to model the\ntemporal evolution of biological systems, but statistical methods for comparing\nDE models to data and for parameter inference are relatively poorly developed.\nThis is especially problematic in the context of biological systems where\nobservations are often noisy and only a small number of time points may be\navailable. 2. The Bayesian approach offers a coherent framework for parameter\ninference that can account for multiple sources of uncertainty, while making\nuse of prior information. It offers a rigorous methodology for parameter\ninference, as well as modelling the link between unobservable model states and\nparameters, and observable quantities. 3. We present deBInfer, a package for\nthe statistical computing environment R, implementing a Bayesian framework for\nparameter inference in DEs. deBInfer provides templates for the DE model, the\nobservation model and data likelihood, and the model parameters and their prior\ndistributions. A Markov chain Monte Carlo (MCMC) procedure processes these\ninputs to estimate the posterior distributions of the parameters and any\nderived quantities, including the model trajectories. Further functionality is\nprovided to facilitate MCMC diagnostics, the visualisation of the posterior\ndistributions of model parameters and trajectories, and the use of compiled DE\nmodels for improved computational performance. 4. The templating approach makes\ndeBInfer applicable to a wide range of DE models. We demonstrate its\napplication to ordinary and delay DE models for population ecology.","url_abs":"http://arxiv.org/abs/1605.00021v3","url_pdf":"http://arxiv.org/pdf/1605.00021v3.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":"debinfer-bayesian-inference-for-dynamical","repo_url":"https://github.com/pboesu/debinfer","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}