{"id":"https://openalex.org/W2155252601","doi":"https://doi.org/10.1109/ssp.2014.6884629","title":"Sequential Monte Carlo samplers for marginal likelihood computation in multiplicative exponential noise models","display_name":"Sequential Monte Carlo samplers for marginal likelihood computation in multiplicative exponential noise models","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2155252601","doi":"https://doi.org/10.1109/ssp.2014.6884629","mag":"2155252601"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2014.6884629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5028197135","display_name":"Onur Dikmen","orcid":"https://orcid.org/0000-0002-6565-4860"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Onur Dikmen","raw_affiliation_strings":["Department of Information and Computer Science, Aalto University, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, Aalto University, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064633247","display_name":"Ali Taylan Cemgil","orcid":"https://orcid.org/0000-0003-4463-8455"},"institutions":[{"id":"https://openalex.org/I4405392","display_name":"Bo\u011fazi\u00e7i University","ror":"https://ror.org/03z9tma90","country_code":"TR","type":"education","lineage":["https://openalex.org/I4405392"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"A. Taylan Cemgil","raw_affiliation_strings":["Computer Engineering Department, Bo\u011fazi\u00e7i University, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering Department, Bo\u011fazi\u00e7i University, Turkey","institution_ids":["https://openalex.org/I4405392"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":0.15945542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"401","issue":null,"first_page":"276","last_page":"279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994000196456909,"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":0.9994000196456909,"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/T10136","display_name":"Statistical Methods and Inference","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9959999918937683,"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":0.7269512414932251},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5305811762809753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5110064744949341},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.5054278373718262},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4995846748352051},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47741615772247314},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"Approximate Bayesian computation","score":0.4668928384780884},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.44381415843963623},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4415081739425659},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4371630847454071},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4363679885864258},{"id":"https://openalex.org/keywords/bayes-factor","display_name":"Bayes factor","score":0.4171033799648285},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39694562554359436},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3834981322288513},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.36683470010757446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3232518136501312},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.2856365442276001},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2234267294406891}],"concepts":[{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.7269512414932251},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5305811762809753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5110064744949341},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.5054278373718262},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4995846748352051},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47741615772247314},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.4668928384780884},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.44381415843963623},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4415081739425659},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4371630847454071},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4363679885864258},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.4171033799648285},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39694562554359436},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3834981322288513},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.36683470010757446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3232518136501312},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2856365442276001},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2234267294406891},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2014.6884629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1513873506","https://openalex.org/W1559926086","https://openalex.org/W1902027874","https://openalex.org/W1979969656","https://openalex.org/W2015749074","https://openalex.org/W2039844283","https://openalex.org/W2144100511","https://openalex.org/W2147357149","https://openalex.org/W2157002241","https://openalex.org/W4233135949","https://openalex.org/W4237791300","https://openalex.org/W6633729224","https://openalex.org/W6682998430"],"related_works":["https://openalex.org/W3114884557","https://openalex.org/W2801810851","https://openalex.org/W75953361","https://openalex.org/W4287550402","https://openalex.org/W3102221624","https://openalex.org/W3207538905","https://openalex.org/W2978031272","https://openalex.org/W2124138033","https://openalex.org/W12931395","https://openalex.org/W1516885591"],"abstract_inverted_index":{"Model":[0],"scoring":[1],"in":[2,70,109],"latent":[3,56,82],"factor":[4,83],"models":[5,84],"is":[6,30,40,77],"essential":[7],"for":[8,81],"a":[9,25,41,50,71],"broad":[10],"spectrum":[11],"of":[12,34,49,65,121],"applications":[13],"such":[14,89],"as":[15,45,90],"clustering,":[16],"change":[17],"point":[18],"detection":[19],"or":[20],"model":[21,28,80],"order":[22],"estimation.":[23],"In":[24,58],"Bayesian":[26],"setting,":[27],"selection":[29],"achieved":[31],"via":[32],"computation":[33,64],"the":[35,55,66,78,86,91,110,119],"marginal":[36,68],"likelihood.":[37],"However,":[38],"this":[39,59],"typically":[42],"challenging":[43],"task":[44],"it":[46],"involves":[47],"calculation":[48],"multidimensional":[51],"integral":[52],"over":[53],"all":[54],"variables.":[57],"paper,":[60],"we":[61],"consider":[62],"approximate":[63],"conditional":[67],"likelihood":[69],"multiplicative":[72],"exponential":[73],"noise":[74],"model,":[75],"which":[76],"generative":[79],"with":[85],"Itakura-Saito":[87],"divergence":[88],"Nonnegative":[92],"Matrix":[93],"Factorization":[94],"(NMF).":[95],"We":[96,117],"show":[97],"that":[98],"standard":[99],"approaches":[100],"are":[101],"not":[102],"accurate":[103],"and":[104],"propose":[105],"two":[106,125],"new":[107],"methods":[108],"sequential":[111],"Monte":[112],"Carlo":[113],"(SMC)":[114],"samplers":[115],"framework.":[116],"explore":[118],"performances":[120],"these":[122],"estimators":[123],"on":[124],"problems.":[126]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}