{"id":"https://openalex.org/W2131617421","doi":"https://doi.org/10.1109/lsp.2011.2106497","title":"Joint Bayesian Decomposition of a Spectroscopic Signal Sequence","display_name":"Joint Bayesian Decomposition of a Spectroscopic Signal Sequence","publication_year":2011,"publication_date":"2011-01-17","ids":{"openalex":"https://openalex.org/W2131617421","doi":"https://doi.org/10.1109/lsp.2011.2106497","mag":"2131617421"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2011.2106497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2011.2106497","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","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/A5058624806","display_name":"Vincent Mazet","orcid":"https://orcid.org/0000-0002-1779-7736"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I68947357","display_name":"Universit\u00e9 de Strasbourg","ror":"https://ror.org/00pg6eq24","country_code":"FR","type":"education","lineage":["https://openalex.org/I68947357"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Vincent Mazet","raw_affiliation_strings":["LSIIT, UMR 7005, CNRS, University of Strasbourg, Illkirch-Graffenstaden, France","LSIIT / University of Strasbourg, Illkirch, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LSIIT, UMR 7005, CNRS, University of Strasbourg, Illkirch-Graffenstaden, France","institution_ids":["https://openalex.org/I68947357","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"LSIIT / University of Strasbourg, Illkirch, France","institution_ids":["https://openalex.org/I68947357"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5058624806"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I68947357"],"apc_list":null,"apc_paid":null,"fwci":1.5597,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.83952306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"18","issue":"3","first_page":"181","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9947999715805054,"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-probability","display_name":"Bayesian probability","score":0.6950583457946777},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6421706676483154},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6204731464385986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5926946997642517},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5890426635742188},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5272271633148193},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5150179862976074},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4777626097202301},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4687139093875885},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4492817223072052},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.439175546169281},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4349386692047119},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35286200046539307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3330044150352478},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2600727677345276},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14564451575279236},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14185753464698792},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.08378514647483826}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6950583457946777},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6421706676483154},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6204731464385986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5926946997642517},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5890426635742188},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5272271633148193},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5150179862976074},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4777626097202301},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4687139093875885},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4492817223072052},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.439175546169281},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4349386692047119},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35286200046539307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3330044150352478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2600727677345276},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14564451575279236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14185753464698792},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.08378514647483826},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lsp.2011.2106497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2011.2106497","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.378.1498","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.1498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://miv.u-strasbg.fr/mazet/publis/mazet11-spl.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-05559344v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05559344","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Signal Processing Letters, 2011, 18 (3), pp.181-184. &#x27E8;10.1109/LSP.2011.2106497&#x27E9;","raw_type":"Journal articles"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1483219881","https://openalex.org/W1531406273","https://openalex.org/W1985093013","https://openalex.org/W1996726072","https://openalex.org/W2000057915","https://openalex.org/W2020999234","https://openalex.org/W2024060531","https://openalex.org/W2035740732","https://openalex.org/W2035756456","https://openalex.org/W2053055324","https://openalex.org/W2068180456","https://openalex.org/W2108820740","https://openalex.org/W2542256003","https://openalex.org/W4292691288","https://openalex.org/W6628960449"],"related_works":["https://openalex.org/W71678127","https://openalex.org/W2157655363","https://openalex.org/W4205763938","https://openalex.org/W2292189132","https://openalex.org/W4288092343","https://openalex.org/W4386114318","https://openalex.org/W2134332527","https://openalex.org/W2888496681","https://openalex.org/W2790979771","https://openalex.org/W4289670352"],"abstract_inverted_index":{"This":[0],"letter":[1],"addresses":[2],"the":[3,26,39,42,48,54,84,87],"problem":[4,57],"of":[5,9,16,41,86],"decomposing":[6],"a":[7,14,61,70],"sequence":[8,44],"spectroscopic":[10],"signals:":[11],"data":[12,82],"are":[13],"series":[15],"(energy":[17],"or":[18],"electromagnetic)":[19],"spectra":[20],"and":[21,31,45],"we":[22],"aim":[23],"to":[24,37,46,50],"estimate":[25],"peak":[27],"parameters":[28,49],"(centers,":[29],"amplitudes,":[30],"widths).":[32],"The":[33,56],"key":[34],"idea":[35],"is":[36,58,67],"perform":[38],"decomposition":[40],"whole":[43],"impose":[47],"evolve":[51],"smoothly":[52],"through":[53],"sequence.":[55],"set":[59],"within":[60],"Bayesian":[62],"framework":[63],"whose":[64],"posterior":[65],"distribution":[66],"sampled":[68],"using":[69],"Markov":[71],"chain":[72],"Monte":[73],"Carlo":[74],"simulated":[75],"annealing":[76],"algorithm.":[77],"Simulations":[78],"conducted":[79],"on":[80],"synthetic":[81],"illustrate":[83],"performance":[85],"method.":[88]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}
