{"id":"https://openalex.org/W4394862796","doi":"https://doi.org/10.1109/msp.2023.3335893","title":"Bayes\u2019 Rule Using Imprecise Probabilities [Lecture Notes]","display_name":"Bayes\u2019 Rule Using Imprecise Probabilities [Lecture Notes]","publication_year":2024,"publication_date":"2024-04-16","ids":{"openalex":"https://openalex.org/W4394862796","doi":"https://doi.org/10.1109/msp.2023.3335893"},"language":"en","primary_location":{"id":"doi:10.1109/msp.2023.3335893","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/msp.2023.3335893","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Magazine","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038829303","display_name":"Branko Risti\u0107","orcid":"https://orcid.org/0000-0001-8561-4412"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Branko Ristic","raw_affiliation_strings":["School of Engineering, RMIT University, Melbourne, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8561-4412","affiliations":[{"raw_affiliation_string":"School of Engineering, RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044312043","display_name":"Alessio Benavoli","orcid":"https://orcid.org/0000-0002-2522-7178"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Alessio Benavoli","raw_affiliation_strings":["School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-2522-7178","affiliations":[{"raw_affiliation_string":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028610249","display_name":"Sanjeev Arulampalam","orcid":"https://orcid.org/0000-0003-3557-3258"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sanjeev Arulampalam","raw_affiliation_strings":["University of Adelaide, Adelaide, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3557-3258","affiliations":[{"raw_affiliation_string":"University of Adelaide, Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038829303"],"corresponding_institution_ids":["https://openalex.org/I82951845"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03843372,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":"1","first_page":"67","last_page":"71"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.994700014591217,"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":0.994700014591217,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9577000141143799,"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/T10050","display_name":"Multi-Criteria Decision Making","score":0.9550999999046326,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.646419882774353},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6183515191078186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46264493465423584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39702779054641724},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.37149953842163086},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3318091034889221}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.646419882774353},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6183515191078186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46264493465423584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39702779054641724},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37149953842163086},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3318091034889221}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/msp.2023.3335893","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/msp.2023.3335893","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W250076511","https://openalex.org/W1969344405","https://openalex.org/W2047733505","https://openalex.org/W2049886280","https://openalex.org/W2123748412","https://openalex.org/W2149440351","https://openalex.org/W2914652127","https://openalex.org/W2966384200","https://openalex.org/W3173138228","https://openalex.org/W4210427261","https://openalex.org/W4213251304","https://openalex.org/W4386074690"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Bayes\u2019":[0],"rule,":[1],"as":[2],"one":[3,85],"of":[4,8,26,29,59,79,102],"the":[5,24,50,53,66,100],"fundamental":[6],"concepts":[7],"statistical":[9],"signal":[10],"processing,":[11],"provides":[12],"a":[13,36,45,56,77,93],"way":[14],"to":[15,64,91,99],"update":[16,51],"our":[17],"belief":[18,40],"about":[19],"an":[20],"event":[21],"based":[22],"on":[23],"arrival":[25],"new":[27],"pieces":[28],"evidence.":[30,67],"Uncertainty":[31],"is":[32,41,63,82],"traditionally":[33],"modeled":[34],"by":[35,44,72],"probability":[37,47,80,94],"distribution.":[38],"Prior":[39],"thus":[42],"expressed":[43],"prior":[46],"distribution,":[48],"while":[49],"involves":[52],"likelihood":[54],"function,":[55],"probabilistic":[57],"expression":[58],"how":[60],"likely":[61],"it":[62],"observe":[65],"It":[68],"has":[69],"been":[70],"argued":[71],"many":[73],"statisticians,":[74],"however,":[75],"that":[76],"broadening":[78],"theory":[81],"required":[83],"because":[84],"may":[86],"not":[87],"always":[88],"be":[89],"able":[90],"provide":[92],"for":[95],"every":[96],"event,":[97],"due":[98],"scarcity":[101],"training":[103],"data.":[104]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}