{"id":"https://openalex.org/W1993058014","doi":"https://doi.org/10.3408/jafst.13.83","title":"Effectiveness of Applying a Bayesian Network to the Offender Profiling: Stochastic Inference of the Indoor-sex-offender\u2019s Employment","display_name":"Effectiveness of Applying a Bayesian Network to the Offender Profiling: Stochastic Inference of the Indoor-sex-offender\u2019s Employment","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W1993058014","doi":"https://doi.org/10.3408/jafst.13.83","mag":"1993058014"},"language":"en","primary_location":{"id":"doi:10.3408/jafst.13.83","doi":"https://doi.org/10.3408/jafst.13.83","is_oa":true,"landing_page_url":"https://doi.org/10.3408/jafst.13.83","pdf_url":"https://www.jstage.jst.go.jp/article/jafst/13/1/13_1_83/_pdf","source":{"id":"https://openalex.org/S1250460","display_name":"Japanese Journal of Forensic Science and Technology","issn_l":"1880-1323","issn":["1880-1323","1881-4689"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Forensic Science and Technology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.jstage.jst.go.jp/article/jafst/13/1/13_1_83/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023636894","display_name":"Wataru Zaitsu","orcid":"https://orcid.org/0000-0003-1017-7283","observed_orcids":["https://orcid.org/0000-0003-1017-7283"]},"institutions":[{"id":"https://openalex.org/I4210095108","display_name":"Aichi Prefectural Police","ror":"https://ror.org/00qtxwz11","country_code":"JP","type":"government","lineage":["https://openalex.org/I4210095108"]},{"id":"https://openalex.org/I4405253543","display_name":"Toyama Prefectural Police","ror":"https://ror.org/00t09ra09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405253543"]},{"id":"https://openalex.org/I4405257468","display_name":"Tottori Prefectural Police","ror":"https://ror.org/05a04tz09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405257468"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wataru Zaitsu","raw_affiliation_strings":["\u5bcc\u5c71\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u5bcc\u5c71\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","institution_ids":["https://openalex.org/I4405253543"]},{"raw_affiliation_string":"Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","institution_ids":["https://openalex.org/I4210095108"]},{"raw_affiliation_string":"Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","institution_ids":["https://openalex.org/I4405257468"]},{"raw_affiliation_string":"Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan","institution_ids":["https://openalex.org/I4405253543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018261108","display_name":"Yusuke Shibuya","orcid":"https://orcid.org/0000-0001-9295-4108","observed_orcids":["https://orcid.org/0000-0001-9295-4108"]},"institutions":[{"id":"https://openalex.org/I4210095108","display_name":"Aichi Prefectural Police","ror":"https://ror.org/00qtxwz11","country_code":"JP","type":"government","lineage":["https://openalex.org/I4210095108"]},{"id":"https://openalex.org/I4405253543","display_name":"Toyama Prefectural Police","ror":"https://ror.org/00t09ra09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405253543"]},{"id":"https://openalex.org/I4405257468","display_name":"Tottori Prefectural Police","ror":"https://ror.org/05a04tz09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405257468"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yu-suke Shibuya","raw_affiliation_strings":["\u9ce5\u53d6\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u9ce5\u53d6\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","institution_ids":["https://openalex.org/I4405257468"]},{"raw_affiliation_string":"Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","institution_ids":["https://openalex.org/I4210095108"]},{"raw_affiliation_string":"Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","institution_ids":["https://openalex.org/I4405257468"]},{"raw_affiliation_string":"Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan","institution_ids":["https://openalex.org/I4405253543"]}]},{"author_position":"last","author":{"id":null,"display_name":"Naohiro Hasegawa","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210095108","display_name":"Aichi Prefectural Police","ror":"https://ror.org/00qtxwz11","country_code":"JP","type":"government","lineage":["https://openalex.org/I4210095108"]},{"id":"https://openalex.org/I4405253543","display_name":"Toyama Prefectural Police","ror":"https://ror.org/00t09ra09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405253543"]},{"id":"https://openalex.org/I4405257468","display_name":"Tottori Prefectural Police","ror":"https://ror.org/05a04tz09","country_code":"JP","type":"government","lineage":["https://openalex.org/I4405257468"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naohiro Hasegawa","raw_affiliation_strings":["\u611b\u77e5\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u611b\u77e5\u770c\u8b66\u5bdf\u672c\u90e8\u5211\u4e8b\u90e8\u79d1\u5b66\u635c\u67fb\u7814\u7a76\u6240","institution_ids":["https://openalex.org/I4210095108"]},{"raw_affiliation_string":"Forensic Science Laboratory, Aichi Prefectural Police H.Q.\ue159 3 2\ue30f 1\ue30f 1 Sannomaru, Nakaku, Nagoya-shi, Aichi 460\ue30f 8502, Japan","institution_ids":["https://openalex.org/I4210095108"]},{"raw_affiliation_string":"Forensic Science Laboratory, Tottori Prefectural Police H.Q.\ue159 2 2\ue30f 12 Chiyomi, Tottori-shi, Tottori 680\ue30f 0911, Japan","institution_ids":["https://openalex.org/I4405257468"]},{"raw_affiliation_string":"Forensic Science Laboratory, Toyama Prefectural Police H.Q.\ue159 1 1\ue30f 7 Shinsogawa, Toyama-shi, Toyama 930\ue30f 8570, Japan","institution_ids":["https://openalex.org/I4405253543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":"1","first_page":"83","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8881999850273132,"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":0.8881999850273132,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.017500000074505806,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10565","display_name":"Psychopathy, Forensic Psychiatry, Sexual Offending","score":0.010900000110268593,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/offender-profiling","display_name":"criminal profiling","score":0.9929999709129333},{"id":"https://openalex.org/keywords/bayesian-networks","display_name":"Bayesian networks","score":0.8339999914169312},{"id":"https://openalex.org/keywords/k2-algorithm","display_name":"K2 algorithm","score":0.7509999871253967},{"id":"https://openalex.org/keywords/minimum-description-length","display_name":"minimum description length","score":0.9629999995231628},{"id":"https://openalex.org/keywords/information-criteria","display_name":"information criteria","score":0.5870000123977661},{"id":"https://openalex.org/keywords/victim-testimony","display_name":"victim testimony","score":0.6299999952316284},{"id":"https://openalex.org/keywords/age-estimation","display_name":"age estimation","score":0.609000027179718},{"id":"https://openalex.org/keywords/behavioral-factors","display_name":"behavioral factors","score":0.6190000176429749}],"concepts":[{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6603385806083679},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.6599510908126831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5609463453292847},{"id":"https://openalex.org/C552425265","wikidata":"https://www.wikidata.org/wiki/Q1191309","display_name":"Offender profiling","level":3,"score":0.5172479748725891},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47790631651878357},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45366954803466797},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4237297773361206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4100780785083771},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4023769795894623},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.38703063130378723},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.359133243560791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31928345561027527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2263743281364441},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3408/jafst.13.83","doi":"https://doi.org/10.3408/jafst.13.83","is_oa":true,"landing_page_url":"https://doi.org/10.3408/jafst.13.83","pdf_url":"https://www.jstage.jst.go.jp/article/jafst/13/1/13_1_83/_pdf","source":{"id":"https://openalex.org/S1250460","display_name":"Japanese Journal of Forensic Science and Technology","issn_l":"1880-1323","issn":["1880-1323","1881-4689"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Forensic Science and Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3408/jafst.13.83","doi":"https://doi.org/10.3408/jafst.13.83","is_oa":true,"landing_page_url":"https://doi.org/10.3408/jafst.13.83","pdf_url":"https://www.jstage.jst.go.jp/article/jafst/13/1/13_1_83/_pdf","source":{"id":"https://openalex.org/S1250460","display_name":"Japanese Journal of Forensic Science and Technology","issn_l":"1880-1323","issn":["1880-1323","1881-4689"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Japanese Journal of Forensic Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.47620001435279846,"display_name":"Peace, justice, and strong institutions"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1993058014.pdf","grobid_xml":"https://content.openalex.org/works/W1993058014.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W628584056","https://openalex.org/W1565302035","https://openalex.org/W1594007058","https://openalex.org/W1980512693","https://openalex.org/W1982651887","https://openalex.org/W1990939301","https://openalex.org/W1993482393","https://openalex.org/W2076766584","https://openalex.org/W2080828514","https://openalex.org/W4231779958"],"related_works":["https://openalex.org/W1594007058","https://openalex.org/W7097507884","https://openalex.org/W2107624888","https://openalex.org/W7127090093","https://openalex.org/W2563379763","https://openalex.org/W7208634582","https://openalex.org/W1850742828","https://openalex.org/W2494673464","https://openalex.org/W7097004611","https://openalex.org/W7020919027"],"abstract_inverted_index":{"Offender":[0],"profiling":[1,244],"is":[2,14,42,78,95,268],"one":[3],"of":[4,7,17,22,55,63,65,72,86,91,101,108,118,144,160,197,206,214,231,241,261],"the":[5,39,45,61,70,99,106,109,119,139,158,161,166,174,190,202,207,212,216,222,229,232,238,242,278,285],"tools":[6],"decision":[8,253],"making":[9,254],"for":[10,57,252,255],"criminal":[11,58],"investigation.":[12,257],"It":[13],"a":[15,53,73,79,123,136],"set":[16],"techniques":[18],"to":[19,97,173,201,248,270,283],"infer":[20],"characteristics":[21,260],"an":[23,66,262],"unknown":[24,263],"offender,":[25],"such":[26],"as":[27,88,151,169,280,282],"sex,":[28],"age":[29,223],"bracket,":[30],"lifestyle,":[31],"psychological":[32],"feature,":[33],"previous":[34,115],"crime,":[35],"inhabited":[36],"area,":[37],"from":[38,60,138,228,287],"information":[40,117,154,251,274],"which":[41],"left":[43],"at":[44],"crime":[46,256],"scene.":[47],"In":[48],"this":[49],"article,":[50],"we":[51,113,134],"proposed":[52],"tool":[54],"decision-making":[56],"investigation":[59],"perspective":[62],"prediction":[64],"uncertain":[67,102],"event":[68],"by":[69,142,165],"use":[71],"Bayesian":[74],"Network":[75],"(BN).":[76],"BN":[77,239],"probability":[80],"model":[81,137,162,208,240],"that":[82,211,237],"describes":[83],"causal":[84],"structure":[85],"events":[87],"chain":[89],"networks":[90],"conditional":[92],"probability,":[93],"and":[94,128,146,153,185,195,276],"capable":[96],"predict":[98,259],"possibility":[100],"events.":[103],"To":[104,258],"examine":[105],"validity":[107,159,209],"constructed":[110,135],"model,":[111,175],"firstly,":[112],"divided":[114],"offenders\u2019":[116],"indoor-sex-offence":[120],"cases":[121],"into":[122],"training":[124,140],"data":[125,130,141,168],"(9,859":[126],"cases)":[127],"validation":[129,167],"(50":[131],"cases).":[132],"Secondly,":[133],"means":[143],"K2":[145],"MDL":[147],"(minimum":[148],"description":[149],"length)":[150],"search-algorithm":[152],"criteria,":[155],"respectively.":[156],"Finally,":[157],"was":[163],"examined":[164],"virtual":[170],"cases.":[171],"According":[172],"21":[176],"target":[177],"variables":[178,184,199],"(16":[179],"behavioral":[180],"variables,":[181],"2":[182],"vehicle":[183],"3":[186],"victim":[187],"variables)":[188],"linked":[189],"explanatory":[191],"variable":[192],"(employment)":[193],"directly,":[194],"most":[196],"these":[198],"related":[200],"employment.":[203],"The":[204,234],"results":[205,235],"showed":[210],"accuracy":[213],"predicting":[215],"employment":[217],"increased":[218],"10%":[219],"higher":[220],"when":[221],"bracket":[224],"could":[225],"be":[226,246],"estimated":[227],"testimony":[230],"victim.":[233],"indicated":[236],"offender":[243,264],"would":[245],"able":[247],"provide":[249],"valuable":[250],"more":[265,272,288],"accurately,":[266],"it":[267],"crucial":[269],"select":[271],"appropriate":[273],"criteria":[275],"develop":[277],"search-algorithm,":[279],"well":[281],"construct":[284],"database":[286],"accurate":[289],"information.":[290]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2016-06-24T00:00:00"}