{"id":"https://openalex.org/W2934180746","doi":"https://doi.org/10.1109/icassp.2019.8683798","title":"Low-pass Filtering as Bayesian Inference","display_name":"Low-pass Filtering as Bayesian Inference","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2934180746","doi":"https://doi.org/10.1109/icassp.2019.8683798","mag":"2934180746"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5102329961","display_name":"Cristobal Valenzuela","orcid":null},"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":"Crist\u00f3bal Valenzuela","raw_affiliation_strings":["Departament of Mathematical Engineering, Universidad de Chile"],"affiliations":[{"raw_affiliation_string":"Departament of Mathematical Engineering, Universidad de Chile","institution_ids":["https://openalex.org/I69737025"]}]},{"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/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":["Departament of Mathematical Engineering, Universidad de Chile","Center for Mathematical Modeling, Universidad de Chile"],"affiliations":[{"raw_affiliation_string":"Departament of Mathematical Engineering, Universidad de Chile","institution_ids":["https://openalex.org/I69737025"]},{"raw_affiliation_string":"Center for Mathematical Modeling, Universidad de Chile","institution_ids":["https://openalex.org/I69737025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102329961"],"corresponding_institution_ids":["https://openalex.org/I69737025"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54086933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3367","last_page":"3371"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9922999739646912,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9922999739646912,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9794999957084656,"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/T11236","display_name":"Control Systems and Identification","score":0.9617000222206116,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6728745698928833},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6003924608230591},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5256879329681396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5236284732818604},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4496401846408844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6728745698928833},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6003924608230591},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5256879329681396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5236284732818604},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4496401846408844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8683798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":32,"referenced_works":["https://openalex.org/W164706946","https://openalex.org/W612979977","https://openalex.org/W2029718163","https://openalex.org/W2061744138","https://openalex.org/W2125849100","https://openalex.org/W2136916741","https://openalex.org/W2141570288","https://openalex.org/W2170912685","https://openalex.org/W2183406623","https://openalex.org/W2276506965","https://openalex.org/W2550412943","https://openalex.org/W2560667801","https://openalex.org/W2596709491","https://openalex.org/W2752783222","https://openalex.org/W2782349669","https://openalex.org/W2782405380","https://openalex.org/W2891650202","https://openalex.org/W2951198167","https://openalex.org/W2963163958","https://openalex.org/W2963204190","https://openalex.org/W2963792467","https://openalex.org/W3098727814","https://openalex.org/W3122364763","https://openalex.org/W4211049957","https://openalex.org/W4289549539","https://openalex.org/W4300293005","https://openalex.org/W6679072756","https://openalex.org/W6685945291","https://openalex.org/W6729760243","https://openalex.org/W6735043530","https://openalex.org/W6744579343","https://openalex.org/W6754135504"],"related_works":["https://openalex.org/W2372267530","https://openalex.org/W2969189870","https://openalex.org/W3015855446","https://openalex.org/W4303857162","https://openalex.org/W2965643117","https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W2010643158","https://openalex.org/W3049691116"],"abstract_inverted_index":{"We":[0,68],"propose":[1],"a":[2,23],"Bayesian":[3,66],"nonparametric":[4],"method":[5],"for":[6,26],"low-pass":[7,43],"filtering":[8],"that":[9,70],"can":[10,49,60,77],"naturally":[11],"handle":[12],"unevenly-sampled":[13],"and":[14,57,76,96],"noise-corrupted":[15],"observations.":[16],"The":[17],"proposed":[18,86],"model":[19,25,72,87],"is":[20,88],"constructed":[21],"as":[22,52],"latent-factor":[24],"time":[27,47,98],"series,":[28],"where":[29],"the":[30,42,46,53,71,85],"latent":[31,55],"factors":[32],"are":[33],"Gaussian":[34],"processes":[35],"with":[36,80],"non-overlapping":[37],"spectra.":[38],"With":[39],"this":[40],"construction,":[41],"version":[44],"of":[45,65],"series":[48],"be":[50,61,78],"identified":[51],"low-frequency":[54],"component,":[56],"therefore":[58],"it":[59],"found":[62],"by":[63],"means":[64],"inference.":[67],"show":[69],"admits":[73],"exact":[74],"training":[75],"implemented":[79],"minimal":[81],"numerical":[82],"approximations.":[83],"Finally,":[84],"validated":[89],"against":[90],"standard":[91],"linear":[92],"filters":[93],"on":[94],"synthetic":[95],"real-world":[97],"series.":[99]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}