{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(7)"],"submitter":["Noskova E"],"funding":["Systems Biology Program by Skoltech","ETH Zürich Postdoctoral Fellowship"],"pubmed_abstract":["Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent techniqu"],"journal":["G3 (Bethesda, Md.)"],"pagination":["jkad080"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10320152"],"repository":["biostudies-literature"],"pubmed_title":["Bayesian optimization for demographic inference."],"pmcid":["PMC10320152"],"pubmed_authors":["Noskova E","Borovitskiy V"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian optimization for demographic inference.","description":"Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent techniqu","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jul","modification":"2026-06-11T05:51:59.558Z","creation":"2025-04-04T18:42:20.753Z"},"accession":"S-EPMC10320152","cross_references":{"pubmed":["37070782"],"doi":["10.1093/g3journal/jkad080"]}}