{"id":"https://openalex.org/W1988999094","doi":"https://doi.org/10.1080/03610910802296646","title":"A Bayesian Change-Point Analysis for Software Reliability Models","display_name":"A Bayesian Change-Point Analysis for Software Reliability Models","publication_year":2008,"publication_date":"2008-09-23","ids":{"openalex":"https://openalex.org/W1988999094","doi":"https://doi.org/10.1080/03610910802296646","mag":"1988999094"},"language":"en","primary_location":{"id":"doi:10.1080/03610910802296646","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910802296646","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and 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/A5013195282","display_name":"Seung-Min Nam","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungmin Nam","raw_affiliation_strings":["Seoul National University","Department of Statistics, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069244560","display_name":"Ji Hwan","orcid":"https://orcid.org/0000-0002-9158-6275"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Ji Hwan Cha","raw_affiliation_strings":["Ewha Woman's University","Department of Statistics , Ewha Woman's University , Seoul , Korea"],"affiliations":[{"raw_affiliation_string":"Ewha Woman's University","institution_ids":["https://openalex.org/I138925566"]},{"raw_affiliation_string":"Department of Statistics , Ewha Woman's University , Seoul , Korea","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007645152","display_name":"Sinsup Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sinsup Cho","raw_affiliation_strings":["Seoul National University","Department of Statistics, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069244560"],"corresponding_institution_ids":["https://openalex.org/I138925566"],"apc_list":null,"apc_paid":null,"fwci":1.062,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79530026,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"37","issue":"9","first_page":"1855","last_page":"1869"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.5363821387290955},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.5277348756790161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.524789035320282},{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.5162906050682068},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4919013977050781},{"id":"https://openalex.org/keywords/monotone-polygon","display_name":"Monotone polygon","score":0.48258793354034424},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47141075134277344},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.4667496979236603},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.4512821137905121},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.4204868674278259},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40006113052368164},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3820246160030365},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3796658515930176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3781437277793884},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.37263140082359314},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.342754989862442},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.261798620223999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20597508549690247}],"concepts":[{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.5363821387290955},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.5277348756790161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.524789035320282},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.5162906050682068},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4919013977050781},{"id":"https://openalex.org/C2834757","wikidata":"https://www.wikidata.org/wiki/Q4925424","display_name":"Monotone polygon","level":2,"score":0.48258793354034424},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47141075134277344},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.4667496979236603},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.4512821137905121},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.4204868674278259},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40006113052368164},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3820246160030365},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3796658515930176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3781437277793884},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37263140082359314},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.342754989862442},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.261798620223999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20597508549690247},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610910802296646","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610910802296646","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W124052719","https://openalex.org/W1564722845","https://openalex.org/W1973982237","https://openalex.org/W1980595883","https://openalex.org/W2013106317","https://openalex.org/W2028227373","https://openalex.org/W2036810709","https://openalex.org/W2047330915","https://openalex.org/W2054038204","https://openalex.org/W2108193906","https://openalex.org/W2134069175","https://openalex.org/W2148534890","https://openalex.org/W2489795861","https://openalex.org/W2914956942","https://openalex.org/W4212891423","https://openalex.org/W4252007106","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4388397594","https://openalex.org/W2356093187","https://openalex.org/W164417758","https://openalex.org/W2186819079","https://openalex.org/W4233488282","https://openalex.org/W4287728430","https://openalex.org/W2794631075","https://openalex.org/W3040609822","https://openalex.org/W1991443374","https://openalex.org/W2783892006"],"abstract_inverted_index":{"In":[0,61],"most":[1],"software":[2],"reliability":[3],"models":[4,132],"which":[5],"utilize":[6],"the":[7,12,16,41,46,64,72,93,97,101,108,113,130,141,145,151],"nonhomogeneous":[8],"Poisson":[9],"process":[10,18],"(NHPP),":[11],"intensity":[13,42,52,73],"function":[14,43,53,74],"for":[15,70,95],"counting":[17],"is":[19,79,90,110],"usually":[20],"assumed":[21],"to":[22,92],"be":[23,55,126,138],"continuous":[24,49],"and":[25,44,50,105,149],"monotone.":[26],"However,":[27],"on":[28],"account":[29],"of":[30,48,100,107],"various":[31],"practical":[32],"reasons,":[33],"there":[34],"may":[35,54],"exist":[36],"some":[37],"change":[38,77,98,122],"points":[39,78,99],"in":[40,57],"thus":[45],"assumption":[47],"monotone":[51],"unrealistic":[56],"many":[58],"real":[59],"situations.":[60],"this":[62],"article,":[63],"Bayesian":[65],"change-point":[66],"approach":[67],"using":[68,112],"beta-mixtures":[69],"modeling":[71],"with":[75,85,129],"possible":[76],"proposed.":[80],"The":[81,103,120],"hidden":[82],"Markov":[83],"model":[84,109,124,143],"non":[86],"constant":[87],"transition":[88],"probabilities":[89],"applied":[91],"beta-mixture":[94],"detecting":[96],"parameters.":[102],"estimation":[104],"interpretation":[106],"illustrated":[111],"Naval":[114],"Tactical":[115],"Data":[116],"System":[117],"(NTDS)":[118],"data.":[119],"proposed":[121,142],"point":[123],"will":[125],"also":[127],"compared":[128],"competing":[131,152],"via":[133],"marginal":[134,147],"likelihood.":[135],"It":[136],"can":[137],"seen":[139],"that":[140],"has":[144],"highest":[146],"likelihood":[148],"outperforms":[150],"models.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-03-06T06:45:51.903784","created_date":"2025-10-10T00:00:00"}