{"id":"https://openalex.org/W138516404","doi":null,"title":"Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks","display_name":"Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks","publication_year":2000,"publication_date":"2000-02-01","ids":{"openalex":"https://openalex.org/W138516404","mag":"138516404"},"language":"en","primary_location":{"id":"mag:138516404","doi":null,"is_oa":false,"landing_page_url":"https://dl.acm.org/citation.cfm?id=871261","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","indexed_in":[],"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/A5009341608","display_name":"Ole J. Mengshoel","orcid":"https://orcid.org/0000-0003-2666-5310","observed_orcids":["https://orcid.org/0000-0003-2666-5310"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ole J. Mengshoel","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2666-5310","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023802054","display_name":"Dan Roth","orcid":"https://orcid.org/0009-0002-1447-5173","observed_orcids":["https://orcid.org/0009-0002-1447-5173"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Roth","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0002-1447-5173","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103481382","display_name":"David C. Wilkins","orcid":"https://orcid.org/0000-0003-2780-0385","observed_orcids":["https://orcid.org/0000-0003-2780-0385"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David C. Wilkins","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":0.0,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.02302315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"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":0.9994999766349792,"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":0.9994999766349792,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11010","display_name":"Logic, Reasoning, and Knowledge","score":9.999999747378752e-05,"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/bayesian-networks","display_name":"Bayesian networks","score":0.847000002861023},{"id":"https://openalex.org/keywords/most-probable-explanation","display_name":"most probable explanation","score":0.9940000176429749},{"id":"https://openalex.org/keywords/local-search","display_name":"local search","score":0.8700000047683716},{"id":"https://openalex.org/keywords/local-optima-avoidance","display_name":"local optima avoidance","score":0.8379999995231628},{"id":"https://openalex.org/keywords/probabilistic-inference","display_name":"probabilistic inference","score":0.5849999785423279}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7513266801834106},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6129580736160278},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5977048873901367},{"id":"https://openalex.org/C135320971","wikidata":"https://www.wikidata.org/wiki/Q1868524","display_name":"Local search (optimization)","level":2,"score":0.5908077955245972},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.5828214883804321},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.550870954990387},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.5332559943199158},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5174227952957153},{"id":"https://openalex.org/C126221529","wikidata":"https://www.wikidata.org/wiki/Q3116070","display_name":"Greedy randomized adaptive search procedure","level":3,"score":0.4627988040447235},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.45821210741996765},{"id":"https://openalex.org/C46011968","wikidata":"https://www.wikidata.org/wiki/Q830527","display_name":"Best-first search","level":4,"score":0.45405274629592896},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.4485189914703369},{"id":"https://openalex.org/C90189156","wikidata":"https://www.wikidata.org/wiki/Q5615481","display_name":"Guided Local Search","level":3,"score":0.4331970810890198},{"id":"https://openalex.org/C139979381","wikidata":"https://www.wikidata.org/wiki/Q17056021","display_name":"Incremental heuristic search","level":4,"score":0.4246967136859894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3765539526939392},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3596274256706238},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30368685722351074},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:138516404","doi":null,"is_oa":false,"landing_page_url":"https://dl.acm.org/citation.cfm?id=871261","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2212302534","https://openalex.org/W7098155895","https://openalex.org/W2035495729","https://openalex.org/W1602205689","https://openalex.org/W2339478915","https://openalex.org/W2278785635","https://openalex.org/W7097525701","https://openalex.org/W1896384678","https://openalex.org/W7098477898","https://openalex.org/W7098243885"],"abstract_inverted_index":{"Heuristic":[0],"search":[1,33,62,91,111,135,144],"and":[2,24,26,77,103,125],"uncertain":[3,17],"information":[4,18],"constitute":[5],"two":[6,116],"central":[7],"Themes":[8],"in":[9,21,47,210],"artificial":[10],"intelligence.":[11],"This":[12],"article":[13],"assumes":[14],"that":[15,88,161],"the":[16,133,155,206],"is":[19],"represented":[20],"Bayesian":[22,49,169],"networks,":[23,182,200],"introduces":[25],"investigates":[27],"a":[28,43,48],"novel":[29,117],"stochastic":[30],"heuristic":[31],"local":[32,61,90,94,134,140],"algorithm,":[34],"Stochastic":[35,38,55,82,162,183],"Greedy":[36,39,56,83,163,184],"Search.":[37],"Search":[40,84,164,185],"searches":[41],"for":[42,168],"most":[44,207],"probable":[45,208],"explanation":[46,148,209],"network.":[50],"The":[51,129],"innovative":[52],"aspects":[53],"of":[54,66,69,100,193],"Search,":[57],"compared":[58,195],"to":[59,92,110,139,154,190,196,214],"existing":[60],"algorithms,":[63,203],"are":[64],"some":[65],"its":[67],"measures":[68,99],"gain,":[70],"certain":[71,74],"noise":[72],"operators,":[73,76],"initialization":[75,121,130,202],"an":[78,104,147],"operator-":[79],"based":[80,120],"variant.":[81],"uses":[85],"noisy":[86],"steps":[87],"allow":[89],"escape":[93],"optima.":[95],"We":[96,113],"introduce":[97,115],"different":[98],"gain(or":[101],"gradient)":[102],"operator-based":[105],"approach,":[106],"giving":[107],"several":[108],"ways":[109],"locally.":[112],"also":[114],"dynamic":[118,127],"programming":[119],"algorithms":[122,131],"denoted":[123],"forward":[124],"backward":[126],"programming.":[128],"start":[132],"at":[136,151],"points":[137],"closer":[138],"optima":[141],"than":[142],"when":[143],"starts":[145],"from":[146,171],"created":[149],"uniformly":[150],"random.":[152],"Comparisons":[153],"state-of-the-art":[156],"inference":[157],"algorithm":[158],"Hugin":[159],"show":[160],"performs":[165],"significantly":[166],"better":[167],"networks":[170],"applications":[172],"as":[173,175],"well":[174],"on":[176],"synthetically":[177],"generated":[178],"networks.":[179],"In":[180,198],"synthetic":[181],"speeds":[186],"up":[187,189],"computation":[188],"three":[191],"orders":[192],"magnitude":[194],"Hugin.":[197],"application":[199],"our":[201],"which":[204],"compute":[205],"bounding":[211],"cases,":[212],"prove":[213],"be":[215],"very":[216],"valuable.":[217]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}