{"id":"https://openalex.org/W4297821037","doi":"https://doi.org/10.48550/arxiv.2209.05581","title":"BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data","display_name":"BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data","publication_year":2022,"publication_date":"2022-09-12","ids":{"openalex":"https://openalex.org/W4297821037","doi":"https://doi.org/10.48550/arxiv.2209.05581"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2209.05581","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05581","pdf_url":"https://arxiv.org/pdf/2209.05581","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.05581","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114084330","display_name":"Karine Tung","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tung, Karine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072982136","display_name":"Steven A. De La Torre","orcid":"https://orcid.org/0000-0002-3303-8270","observed_orcids":["https://orcid.org/0000-0002-3303-8270"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De La Torre, Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048323184","display_name":"Mohamed El Mistiri","orcid":"https://orcid.org/0000-0002-0973-812X","observed_orcids":["https://orcid.org/0000-0002-0973-812X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mistiri, Mohamed El","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082902317","display_name":"Rebecca Braga De Braganca","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Braganca, Rebecca Braga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078089549","display_name":"Eric B. Hekler","orcid":"https://orcid.org/0000-0002-7434-0775","observed_orcids":["https://orcid.org/0000-0002-7434-0775"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hekler, Eric","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020862899","display_name":"Misha Pavel C","orcid":"https://orcid.org/0000-0002-8023-6405","observed_orcids":["https://orcid.org/0000-0002-8023-6405","https://orcid.org/0000-0002-4064-3436"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavel, Misha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067879371","display_name":"Daniel E. Rivera","orcid":"https://orcid.org/0000-0002-3141-0577","observed_orcids":["https://orcid.org/0000-0002-3141-0577"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rivera, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021150619","display_name":"Pedja Klasnja","orcid":"https://orcid.org/0000-0002-4570-703X","observed_orcids":["https://orcid.org/0000-0002-4570-703X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klasnja, Pedja","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011189768","display_name":"Donna Spruijt\u2010Metz","orcid":"https://orcid.org/0000-0002-3884-4300","observed_orcids":["https://orcid.org/0000-0002-3884-4300"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spruijt-Metz, Donna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023142437","display_name":"Benjamin M. Marlin","orcid":"https://orcid.org/0000-0002-2626-3410","observed_orcids":["https://orcid.org/0000-0002-2626-3410"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marlin, Benjamin M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9833999872207642,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9833999872207642,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.006599999964237213,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.001500000013038516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/domain-specific-languages","display_name":"domain-specific languages","score":0.7609999775886536},{"id":"https://openalex.org/keywords/probabilistic-programming","display_name":"probabilistic programming","score":0.675000011920929},{"id":"https://openalex.org/keywords/dynamic-bayesian-networks","display_name":"dynamic Bayesian networks","score":0.9929999709129333},{"id":"https://openalex.org/keywords/compiler-optimization","display_name":"compiler optimization","score":0.5099999904632568},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.8159999847412109},{"id":"https://openalex.org/keywords/missing-data","display_name":"missing data","score":0.9330000281333923},{"id":"https://openalex.org/keywords/multivariate-time-series","display_name":"multivariate time series","score":0.9710000157356262},{"id":"https://openalex.org/keywords/mobile-health-data","display_name":"mobile health data","score":0.9359999895095825},{"id":"https://openalex.org/keywords/data-heterogeneity","display_name":"data heterogeneity","score":0.5220000147819519}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.810399055480957},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5506522059440613},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.539550244808197},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5087524056434631},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4952344000339508},{"id":"https://openalex.org/C133162039","wikidata":"https://www.wikidata.org/wiki/Q1061077","display_name":"Code generation","level":3,"score":0.4726623296737671},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.4629128575325012},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40259718894958496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34023234248161316},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32289889454841614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3219757080078125},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3180690407752991},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2209.05581","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05581","pdf_url":"https://arxiv.org/pdf/2209.05581","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2209.05581","doi":"https://doi.org/10.48550/arxiv.2209.05581","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2209.05581","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.05581","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05581","pdf_url":"https://arxiv.org/pdf/2209.05581","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3699122668","display_name":"Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and Personalization","funder_award_id":"U01CA229445","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G9000916032","display_name":"mHealth Center for Discovery, Optimization, and Translation of Temporally-Precise Interventions (mDOT)","funder_award_id":"P41EB028242","funder_id":"https://openalex.org/F4320337363","funder_display_name":"National Institute of Biomedical Imaging and Bioengineering"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"},{"id":"https://openalex.org/F4320337363","display_name":"National Institute of Biomedical Imaging and Bioengineering","ror":"https://ror.org/00372qc85"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4386944599","https://openalex.org/W7119557147","https://openalex.org/W3010692414","https://openalex.org/W2998115353","https://openalex.org/W7120642164","https://openalex.org/W2121388581","https://openalex.org/W2887083126","https://openalex.org/W3031509198","https://openalex.org/W2269254960","https://openalex.org/W2342871354"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"present":[4],"BayesLDM,":[5],"a":[6,15,31,55,72],"system":[7,133,153],"for":[8,22,40,83,87],"Bayesian":[9,51,65,85],"longitudinal":[10,115],"data":[11,28,79,116],"modeling":[12,17,23,49,105],"consisting":[13],"of":[14,50,63,77,113,122,138,148,151],"high-level":[16],"language":[18],"with":[19,30,54,75],"specific":[20,56],"features":[21],"complex":[24,114],"multivariate":[25],"time":[26],"series":[27],"coupled":[29],"compiler":[32,70],"that":[33,109],"can":[34],"produce":[35],"optimized":[36],"probabilistic":[37,126],"program":[38],"code":[39,82],"performing":[41,84],"inference":[42,86,127,141],"in":[43,107],"the":[44,59,99,111,120,131,136,149,152],"specified":[45],"model.":[46],"BayesLDM":[47,69,132],"supports":[48],"network":[52],"models":[53],"focus":[57],"on":[58],"efficient,":[60],"declarative":[61],"specification":[62,74],"dynamic":[64],"Networks":[66],"(DBNs).":[67],"The":[68],"combines":[71],"model":[73,89],"inspection":[76],"available":[78],"and":[80,140,143,157],"outputs":[81],"unknown":[88],"parameters":[90],"while":[91],"simultaneously":[92],"handling":[93],"missing":[94],"data.":[95,162],"These":[96],"capabilities":[97],"have":[98],"potential":[100],"to":[101,154],"significantly":[102],"accelerate":[103],"iterative":[104],"workflows":[106],"domains":[108],"involve":[110],"analysis":[112],"by":[117],"abstracting":[118],"away":[119],"process":[121],"producing":[123],"computationally":[124],"efficient":[125],"code.":[128],"We":[129],"describe":[130],"components,":[134],"evaluate":[135],"efficiency":[137],"representation":[139],"optimizations":[142],"provide":[144],"an":[145],"illustrative":[146],"example":[147],"application":[150],"analyzing":[155],"heterogeneous":[156],"partially":[158],"observed":[159],"mobile":[160],"health":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2022-10-01T00:00:00"}