{"id":"https://openalex.org/W3203882154","doi":"https://doi.org/10.1109/la-cci48322.2021.9769834","title":"Bayesian autoregressive spectral estimation","display_name":"Bayesian autoregressive spectral estimation","publication_year":2021,"publication_date":"2021-11-02","ids":{"openalex":"https://openalex.org/W3203882154","doi":"https://doi.org/10.1109/la-cci48322.2021.9769834","mag":"3203882154"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci48322.2021.9769834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci48322.2021.9769834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"},"type":"preprint","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/A5065305058","display_name":"Alejandro Cuevas","orcid":"https://orcid.org/0000-0001-6507-1334"},"institutions":[{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":true,"raw_author_name":"Alejandro Cuevas","raw_affiliation_strings":["Universidad de Chile,Department of Mathematical Engineering"],"affiliations":[{"raw_affiliation_string":"Universidad de Chile,Department of Mathematical Engineering","institution_ids":["https://openalex.org/I69737025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065148192","display_name":"Sebasti\u00e1n L\u00f3pez","orcid":"https://orcid.org/0000-0003-0389-0902"},"institutions":[{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Sebastian Lopez","raw_affiliation_strings":["Universidad de Chile,Department of Mathematical Engineering"],"affiliations":[{"raw_affiliation_string":"Universidad de Chile,Department of Mathematical Engineering","institution_ids":["https://openalex.org/I69737025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103001848","display_name":"Danilo P. Mandic","orcid":"https://orcid.org/0000-0001-8432-3963"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Danilo Mandic","raw_affiliation_strings":["Imperial College London,Department of Electrical and Electronic Engineering"],"affiliations":[{"raw_affiliation_string":"Imperial College London,Department of Electrical and Electronic Engineering","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020822083","display_name":"Felipe Tobar","orcid":"https://orcid.org/0000-0003-2486-3583"},"institutions":[{"id":"https://openalex.org/I4387155550","display_name":"Center for Mathematical Modeling","ror":"https://ror.org/00wz2vk41","country_code":null,"type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4387155550","https://openalex.org/I69737025"]},{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Felipe Tobar","raw_affiliation_strings":["Universidad de Chile,Center for Mathematical Modeling","Center for Mathematical Modeling, Universidad de Chile","Initiative for Data & Artificial Intelligence, Universidad de Chile"],"affiliations":[{"raw_affiliation_string":"Universidad de Chile,Center for Mathematical Modeling","institution_ids":["https://openalex.org/I69737025","https://openalex.org/I4387155550"]},{"raw_affiliation_string":"Center for Mathematical Modeling, Universidad de Chile","institution_ids":["https://openalex.org/I69737025"]},{"raw_affiliation_string":"Initiative for Data & Artificial Intelligence, Universidad de Chile","institution_ids":["https://openalex.org/I69737025"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065305058"],"corresponding_institution_ids":["https://openalex.org/I69737025"],"apc_list":null,"apc_paid":null,"fwci":0.14110358,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55643007,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9988999962806702,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9988999962806702,"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":0.998199999332428,"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/T10320","display_name":"Neural Networks and Applications","score":0.9945999979972839,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8803544044494629},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.6123083233833313},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5746633410453796},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.5238000154495239},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5213369131088257},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.499375581741333},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.49894118309020996},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4702885150909424},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4592234492301941},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4087270498275757},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3976510167121887},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35628217458724976},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3256577253341675}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8803544044494629},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.6123083233833313},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5746633410453796},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5213369131088257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.499375581741333},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.49894118309020996},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4702885150909424},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4592234492301941},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4087270498275757},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3976510167121887},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35628217458724976},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3256577253341675},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"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":1,"locations":[{"id":"doi:10.1109/la-cci48322.2021.9769834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci48322.2021.9769834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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":26,"referenced_works":["https://openalex.org/W181306761","https://openalex.org/W1746819321","https://openalex.org/W1974618482","https://openalex.org/W1975370672","https://openalex.org/W2077845604","https://openalex.org/W2117787517","https://openalex.org/W2135194391","https://openalex.org/W2140971281","https://openalex.org/W2183406623","https://openalex.org/W2217402295","https://openalex.org/W2313953460","https://openalex.org/W2592023122","https://openalex.org/W2752783222","https://openalex.org/W2891650202","https://openalex.org/W2951198167","https://openalex.org/W2963163958","https://openalex.org/W2963977107","https://openalex.org/W3101283031","https://openalex.org/W3122364763","https://openalex.org/W4211049957","https://openalex.org/W4289549539","https://openalex.org/W4292963524","https://openalex.org/W6679529799","https://openalex.org/W6685945291","https://openalex.org/W6744579343","https://openalex.org/W6754135504"],"related_works":["https://openalex.org/W1976222415","https://openalex.org/W2147584239","https://openalex.org/W2552640122","https://openalex.org/W262848098","https://openalex.org/W1917904586","https://openalex.org/W2488177955","https://openalex.org/W1488610482","https://openalex.org/W2169599856","https://openalex.org/W2783211270","https://openalex.org/W2355015151"],"abstract_inverted_index":{"Autoregressive":[0],"(AR)":[1],"time":[2,21],"series":[3,22],"models":[4],"are":[5,41],"widely":[6],"used":[7],"in":[8,35],"parametric":[9],"spectral":[10,16,130,143],"estimation":[11,131,144],"(SE),":[12],"where":[13],"the":[14,20,28,49,59,62,75,79,84,89,98,104,117,126,157],"power":[15],"density":[17],"(PSD)":[18],"of":[19,27,51,61,88,140],"is":[23,33,152],"approximated":[24],"by":[25,82,101,111],"that":[26,125,136],"best-fit":[29],"AR":[30,39,80,90,105],"model,":[31],"which":[32],"available":[34],"closed":[36],"form.":[37],"Since":[38],"parameters":[40,91],"usually":[42],"found":[43],"via":[44],"maximum-likelihood,":[45],"least":[46],"squares":[47],"or":[48],"method":[50],"moments,":[52],"AR-based":[53],"SE":[54],"fails":[55],"to":[56,73,78,92,97],"account":[57],"for":[58],"uncertainty":[60,76,96],"approximate":[63],"PSD,":[64],"and":[65,156,162],"thus":[66],"only":[67],"yields":[68],"point":[69,134],"estimates.":[70],"We":[71],"propose":[72],"handle":[74],"related":[77],"approximation":[81,100],"finding":[83],"full":[85],"posterior":[86],"distribution":[87],"then":[93],"propagate":[94],"this":[95,109],"PSD":[99],"integrating":[102],"out":[103],"parameters;":[106],"we":[107,123],"implement":[108],"concept":[110],"assuming":[112],"two":[113],"different":[114],"priors":[115],"over":[116],"model":[118],"noise.":[119],"Through":[120],"practical":[121],"experiments,":[122],"show":[124],"proposed":[127],"Bayesian":[128],"autoregressive":[129,142],"(BASE)":[132],"provides":[133],"estimates":[135],"follow":[137],"closely":[138],"those":[139],"standard":[141],"(ASE),":[145],"while":[146],"also":[147],"providing":[148],"error":[149],"bars.":[150],"BASE":[151],"validated":[153],"against":[154],"ASE":[155],"Periodogram":[158],"on":[159],"both":[160],"synthetic":[161],"real-world":[163],"signals.":[164]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
