{"id":"https://openalex.org/W2188980425","doi":null,"title":"Estimating the Parameters of Randomly Interleaved","display_name":"Estimating the Parameters of Randomly Interleaved","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2188980425","mag":"2188980425"},"language":"ca","primary_location":{"id":"mag:2188980425","doi":null,"is_oa":false,"landing_page_url":"http://ieeexplore.ieee.org/iel5/5360416/5360417/05360423.pdf","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","is_traditional":true,"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/A5068280922","display_name":"Daniel Gillblad","orcid":"https://orcid.org/0000-0001-8952-3542","observed_orcids":["https://orcid.org/0000-0001-8952-3542"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Gillblad","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8952-3542","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056257612","display_name":"Diogo R. Ferreira","orcid":"https://orcid.org/0000-0001-5818-9406","observed_orcids":["https://orcid.org/0000-0001-5818-9406"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diogo R. Ferreira","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5818-9406","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":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.8945000171661377,"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":0.8945000171661377,"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/T11236","display_name":"Control Systems and Identification","score":0.014399999752640724,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T13500","display_name":"Probability and Statistical Research","score":0.011699999682605267,"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/parameter-estimation","display_name":"parameter estimation","score":0.7749999761581421},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.8140000104904175},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.33000001311302185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6559113264083862},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6541919708251953},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.548879861831665},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5237940549850464},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4406803548336029},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4263383746147156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3093416690826416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2793838083744049},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2188980425","doi":null,"is_oa":false,"landing_page_url":"http://ieeexplore.ieee.org/iel5/5360416/5360417/05360423.pdf","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":18,"referenced_works":["https://openalex.org/W1522502157","https://openalex.org/W1528056001","https://openalex.org/W1636244751","https://openalex.org/W1922746676","https://openalex.org/W1975764300","https://openalex.org/W1988197914","https://openalex.org/W2038885294","https://openalex.org/W2074673068","https://openalex.org/W2075978670","https://openalex.org/W2125838338","https://openalex.org/W2134937538","https://openalex.org/W2135458074","https://openalex.org/W2149429918","https://openalex.org/W2151635921","https://openalex.org/W2168063828","https://openalex.org/W2332363036","https://openalex.org/W3123625224","https://openalex.org/W3128806671"],"related_works":["https://openalex.org/W1994251792","https://openalex.org/W1597533204","https://openalex.org/W2090970706","https://openalex.org/W2092456117","https://openalex.org/W874730111","https://openalex.org/W2006093883","https://openalex.org/W1562600098","https://openalex.org/W7098814557","https://openalex.org/W2306210182","https://openalex.org/W4242608753"],"abstract_inverted_index":{"Sequences":[0],"that":[1,61],"can":[2],"be":[3],"assumed":[4],"to":[5,99],"have":[6],"been":[7],"gener-":[8],"ated":[9],"by":[10],"a":[11,30,47,75,90],"number":[12,31],"of":[13,32,43,82,102,110],"Markov":[14],"models,":[15],"whose":[16],"outputs":[17],"are":[18,26,64,71],"randomly":[19],"interleaved":[20],"but":[21],"where":[22,35,94],"the":[23,59,78,83,97,100,106],"actual":[24],"sources":[25,63],"hidden,":[27],"occur":[28],"in":[29,105],"practical":[33,48],"situations":[34],"data":[36,56],"is":[37],"captured":[38],"as":[39],"an":[40,111],"unlabeled":[41],"stream":[42],"events.":[44],"We":[45],"present":[46],"method":[49],"for":[50],"estimating":[51],"model":[52],"parameters":[53],"on":[54,67,77],"large":[55],"sets":[57],"under":[58],"assumption":[60],"all":[62],"identical.":[65],"Results":[66],"representa-":[68],"tive":[69],"examples":[70],"presented,":[72],"together":[73],"with":[74],"discussion":[76],"accuracy":[79],"and":[80],"performance":[81],"proposed":[84],"estimation":[85],"algorithms.":[86],"Finally,":[87],"we":[88,95],"describe":[89],"real-world":[91],"case":[92],"study":[93],"apply":[96],"technique":[98],"sequence":[101],"events":[103],"recorded":[104],"technical":[107],"support":[108],"database":[109],"IT":[112],"vendor.":[113],"I.":[114],"INTRODUCTION":[115]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}