{"id":"https://openalex.org/W4416685318","doi":"https://doi.org/10.48550/arxiv.2509.00753","title":"FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging","display_name":"FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging","publication_year":2025,"publication_date":"2025-08-31","ids":{"openalex":"https://openalex.org/W4416685318","doi":"https://doi.org/10.48550/arxiv.2509.00753"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.00753","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.00753","pdf_url":"https://arxiv.org/pdf/2509.00753","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.00753","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023679641","display_name":"Florian Frommlet","orcid":"https://orcid.org/0000-0003-4104-1768"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Frommlet, Florian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066200284","display_name":"Jon Lachmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lachmann, Jon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011128600","display_name":"Geir Storvik","orcid":"https://orcid.org/0000-0001-8198-1426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Storvik, Geir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015268618","display_name":"Aliaksandr Hubin","orcid":"https://orcid.org/0000-0002-3244-6571"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hubin, Aliaksandr","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023679641"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.4715999960899353,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.4715999960899353,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.12229999899864197,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.11309999972581863,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6686999797821045},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.582099974155426},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.578499972820282},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.46549999713897705},{"id":"https://openalex.org/keywords/reversible-jump-markov-chain-monte-carlo","display_name":"Reversible-jump Markov chain Monte Carlo","score":0.45329999923706055},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.446399986743927},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.4323999881744385},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4246000051498413},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.40230000019073486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680400013923645},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6686999797821045},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.582099974155426},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5005999803543091},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C2780591659","wikidata":"https://www.wikidata.org/wiki/Q17083869","display_name":"Reversible-jump Markov chain Monte Carlo","level":4,"score":0.45329999923706055},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.4323999881744385},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4246000051498413},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4138000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40939998626708984},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.40230000019073486},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.3783999979496002},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.35659998655319214},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.3488999903202057},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.3418999910354614},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.3361000120639801},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3208000063896179},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.2904999852180481},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28349998593330383},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C149569020","wikidata":"https://www.wikidata.org/wiki/Q25098598","display_name":"Bayesian average","level":5,"score":0.26460000872612},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26019999384880066},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.00753","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.00753","pdf_url":"https://arxiv.org/pdf/2509.00753","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.00753","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.00753","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.00753","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.00753","pdf_url":"https://arxiv.org/pdf/2509.00753","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"FBMS":[1,110],"R":[2],"package":[3,29],"facilitates":[4],"Bayesian":[5,50,74],"model":[6,9,22],"selection":[7],"and":[8,85,95,114,155],"averaging":[10],"in":[11,45,117],"complex":[12,147],"regression":[13],"settings":[14],"by":[15],"employing":[16],"a":[17,58,132],"variety":[18],"of":[19,73,88,99,109,124,128,135],"Monte":[20,37],"Carlo":[21,38],"exploration":[23],"methods.":[24],"At":[25],"its":[26],"core,":[27],"the":[28,71,82,97,106,125,140],"implements":[30],"an":[31],"efficient":[32],"Mode":[33],"Jumping":[34],"Markov":[35],"Chain":[36],"(MJMCMC)":[39],"algorithm,":[40],"designed":[41],"to":[42,145,151],"improve":[43],"mixing":[44],"multi-modal":[46],"posterior":[47,93],"landscapes":[48],"within":[49],"generalized":[51,75],"linear":[52],"models.":[53,129,158],"In":[54],"addition,":[55],"it":[56],"provides":[57],"genetically":[59],"modified":[60],"MJMCMC":[61],"(GMJMCMC)":[62],"algorithm":[63,83],"that":[64],"introduces":[65],"nonlinear":[66,76],"feature":[67],"generation,":[68],"thereby":[69],"enabling":[70],"estimation":[72],"models":[77,100],"(BGNLMs).":[78],"Within":[79],"this":[80],"framework,":[81],"maintains":[84],"updates":[86],"populations":[87],"transformed":[89],"features,":[90],"computes":[91],"their":[92],"probabilities,":[94],"evaluates":[96],"posteriors":[98],"constructed":[101],"from":[102],"them.":[103],"We":[104],"demonstrate":[105],"effective":[107],"use":[108],"for":[111],"both":[112],"inferential":[113],"predictive":[115],"modeling":[116,148],"Gaussian":[118],"regression,":[119],"focusing":[120],"on":[121],"different":[122],"instances":[123],"BGNLM":[126],"class":[127],"Furthermore,":[130],"through":[131],"broad":[133],"set":[134],"applications,":[136],"we":[137],"illustrate":[138],"how":[139],"methodology":[141],"can":[142],"be":[143],"extended":[144],"increasingly":[146],"scenarios,":[149],"extending":[150],"other":[152],"response":[153],"distributions":[154],"mixed":[156],"effect":[157]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}