{"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/bambi-blind-accelerated-multimodal-bayesian","title":"BAMBI: blind accelerated multimodal Bayesian inference","arxiv_id":"1110.2997","date":"2011-10-13","proceeding":null,"authors":["Philip Graff","Farhan Feroz","Michael P. Hobson","Anthony Lasenby"],"abstract":"In this paper we present an algorithm for rapid Bayesian analysis that\ncombines the benefits of nested sampling and artificial neural networks. The\nblind accelerated multimodal Bayesian inference (BAMBI) algorithm implements\nthe MultiNest package for nested sampling as well as the training of an\nartificial neural network (NN) to learn the likelihood function. In the case of\ncomputationally expensive likelihoods, this allows the substitution of a much\nmore rapid approximation in order to increase significantly the speed of the\nanalysis. We begin by demonstrating, with a few toy examples, the ability of a\nNN to learn complicated likelihood surfaces. BAMBI's ability to decrease\nrunning time for Bayesian inference is then demonstrated in the context of\nestimating cosmological parameters from Wilkinson Microwave Anisotropy Probe\nand other observations. We show that valuable speed increases are achieved in\naddition to obtaining NNs trained on the likelihood functions for the different\nmodel and data combinations. These NNs can then be used for an even faster\nfollow-up analysis using the same likelihood and different priors. This is a\nfully general algorithm that can be applied, without any pre-processing, to\nother problems with computationally expensive likelihood functions.","url_abs":"http://arxiv.org/abs/1110.2997v2","url_pdf":"http://arxiv.org/pdf/1110.2997v2.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":"bambi-blind-accelerated-multimodal-bayesian","repo_url":"https://github.com/DarkMachines/pyBAMBI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bambi-blind-accelerated-multimodal-bayesian","repo_url":"https://github.com/igomezv/neuralike","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}