{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hart JDA"],"funding":["European Research Council","NIA NIH HHS","NIMH NIH HHS","Natural Environment Research Council","Engineering and Physical Sciences Research Council"],"pagination":["2411-2420"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10923527"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(9)"],"pubmed_abstract":["Animal social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of o"],"journal":["Methods in ecology and evolution"],"pubmed_title":["BISoN: A Bayesian Framework for Inference of Social Networks."],"pmcid":["PMC10923527"],"funding_grant_id":["NE/S009914/1","R01 AG060931","EP/R513210/1","864461","NE/S010327/1","R01 MH118203"],"pubmed_authors":["Franks DW","Weiss MN","Hart JDA","Brent LJN"],"additional_accession":[]},"is_claimable":false,"name":"BISoN: A Bayesian Framework for Inference of Social Networks.","description":"Animal social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of o","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Sep","modification":"2026-04-17T13:28:20.35Z","creation":"2025-04-07T12:24:59.058Z"},"accession":"S-EPMC10923527","cross_references":{"pubmed":["38463700"],"doi":["10.1111/2041-210x.14171"]}}