{"id":"https://openalex.org/W2152553986","doi":"https://doi.org/10.1162/089976699300016395","title":"Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction","display_name":"Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction","publication_year":1999,"publication_date":"1999-07-01","ids":{"openalex":"https://openalex.org/W2152553986","doi":"https://doi.org/10.1162/089976699300016395","mag":"2152553986"},"language":"en","primary_location":{"id":"doi:10.1162/089976699300016395","is_oa":false,"landing_page_url":"https://doi.org/10.1162/089976699300016395","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","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/A5013617347","display_name":"Matthew Brand","orcid":"https://orcid.org/0000-0001-7698-4884"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Matthew Brand","raw_affiliation_strings":["Mitsubishi Electric Research Labs, Cambridge Research Center, Cambridge, MA 02139, U.S.A"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs, Cambridge Research Center, Cambridge, MA 02139, U.S.A","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5013617347"],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":14.7829,"has_fulltext":false,"cited_by_count":159,"citation_normalized_percentile":{"value":0.99007385,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"11","issue":"5","first_page":"1155","last_page":"1182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9962999820709229,"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":0.9962999820709229,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9933000206947327,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.989799976348877,"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/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6606321930885315},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.6135491132736206},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5661963224411011},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5292724370956421},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5076512098312378},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.5068283677101135},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.48703843355178833},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.48146143555641174},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4555765688419342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4484993815422058},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4170162081718445},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41545993089675903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39105841517448425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37266528606414795},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.26172709465026855},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.198125958442688},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1872522532939911}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6606321930885315},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6135491132736206},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5661963224411011},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5292724370956421},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5076512098312378},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.5068283677101135},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.48703843355178833},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.48146143555641174},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4555765688419342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4484993815422058},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4170162081718445},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41545993089675903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39105841517448425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37266528606414795},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26172709465026855},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.198125958442688},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1872522532939911},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1162/089976699300016395","is_oa":false,"landing_page_url":"https://doi.org/10.1162/089976699300016395","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W34473658","https://openalex.org/W113717426","https://openalex.org/W1482691566","https://openalex.org/W1506276931","https://openalex.org/W1551198852","https://openalex.org/W1554663460","https://openalex.org/W1565083249","https://openalex.org/W1615454278","https://openalex.org/W1825362480","https://openalex.org/W1993744672","https://openalex.org/W2005012045","https://openalex.org/W2010894431","https://openalex.org/W2033565080","https://openalex.org/W2068728923","https://openalex.org/W2068970468","https://openalex.org/W2074251375","https://openalex.org/W2092866774","https://openalex.org/W2095684453","https://openalex.org/W2112226223","https://openalex.org/W2113664036","https://openalex.org/W2114766824","https://openalex.org/W2120972216","https://openalex.org/W2121373282","https://openalex.org/W2124641450","https://openalex.org/W2125389748","https://openalex.org/W2125838338","https://openalex.org/W2126163471","https://openalex.org/W2127893047","https://openalex.org/W2128160875","https://openalex.org/W2145173023","https://openalex.org/W2152265091","https://openalex.org/W2152511329","https://openalex.org/W2167277498","https://openalex.org/W2963844860","https://openalex.org/W2982720039","https://openalex.org/W3211400184","https://openalex.org/W4285719527","https://openalex.org/W4298164899"],"related_works":["https://openalex.org/W4385957992","https://openalex.org/W2045588782","https://openalex.org/W2758307934","https://openalex.org/W4298063201","https://openalex.org/W2124821524","https://openalex.org/W4324297246","https://openalex.org/W2147511255","https://openalex.org/W4220988758","https://openalex.org/W2992724381","https://openalex.org/W2152553986"],"abstract_inverted_index":{"We":[0,59],"introduce":[1],"an":[2],"entropic":[3],"prior":[4,20],"for":[5,12,24,63],"multinomial":[6],"parameter":[7,57,113],"estimation":[8,40,114],"problems":[9],"and":[10,27,73,100,117,152],"solve":[11],"its":[13],"maximum":[14],"a":[15,22,65,76],"posteriori":[16],"(MAP)":[17],"estimator.":[18],"The":[19],"is":[21,54],"bias":[23],"maximally":[25,101],"structured":[26],"minimally":[28],"ambiguous":[29],"models.":[30],"In":[31,143],"conditional":[32],"probability":[33,86],"models":[34,135,148],"with":[35,75,158,164],"hidden":[36,129,159],"state,":[37],"iterative":[38],"MAP":[39],"drives":[41],"weakly":[42],"supported":[43],"parameters":[44,72],"toward":[45],"extinction,":[46],"effectively":[47],"turning":[48],"them":[49],"off.":[50],"Thus,":[51],"structure":[52,69,124],"discovery":[53],"folded":[55],"into":[56],"estimation.":[58],"then":[60],"establish":[61],"criteria":[62],"simplifying":[64],"probabilistic":[66],"model's":[67],"graphical":[68],"by":[70,93],"trimming":[71],"states,":[74],"guarantee":[77],"that":[78,154,161],"any":[79],"such":[80],"deletion":[81],"will":[82],"increase":[83,102],"the":[84,88,95,103,133,146],"posterior":[85,104],"of":[87,119],"model.":[89,96],"Trimming":[90],"accelerates":[91],"learning":[92],"sparsifying":[94],"All":[97],"operations":[98],"monotonically":[99],"probability,":[105],"yielding":[106],"structure-learning":[107],"algorithms":[108],"only":[109],"slightly":[110],"slower":[111],"than":[112,122],"via":[115],"expectation-maximization":[116],"orders":[118],"magnitude":[120],"faster":[121],"search-based":[123],"induction.":[125],"When":[126],"applied":[127],"to":[128,139],"Markov":[130],"model":[131],"training,":[132],"resulting":[134,147],"show":[136],"superior":[137],"generalization":[138],"held-out":[140],"test":[141],"data.":[142],"many":[144],"cases":[145],"are":[149,156],"so":[150],"sparse":[151],"concise":[153],"they":[155],"interpretable,":[157],"states":[160],"strongly":[162],"correlate":[163],"meaningful":[165],"categories.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
