{"id":"https://openalex.org/W3010883688","doi":"https://doi.org/10.1109/camsap45676.2019.9022469","title":"Change Detection and Gaussian Process Inference in Piecewise Stationary Environments Under Noisy Inputs","display_name":"Change Detection and Gaussian Process Inference in Piecewise Stationary Environments Under Noisy Inputs","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3010883688","doi":"https://doi.org/10.1109/camsap45676.2019.9022469","mag":"3010883688"},"language":"en","primary_location":{"id":"doi:10.1109/camsap45676.2019.9022469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap45676.2019.9022469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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/A5009495271","display_name":"Tales Imbiriba","orcid":"https://orcid.org/0000-0002-2626-2039"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tales Imbiriba","raw_affiliation_strings":["Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088554375","display_name":"Gerald LaMountain","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gerald LaMountain","raw_affiliation_strings":["Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101478680","display_name":"Peng Wu","orcid":"https://orcid.org/0000-0001-7117-3804"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Wu","raw_affiliation_strings":["Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083261801","display_name":"Deniz Erdo\u011fmu\u015f","orcid":"https://orcid.org/0000-0002-1114-3539"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deniz Erdogmus","raw_affiliation_strings":["Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070127242","display_name":"Pau Closas","orcid":"https://orcid.org/0000-0002-5960-6600"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pau Closas","raw_affiliation_strings":["Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering department, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5009495271"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67999488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2017","issue":null,"first_page":"530","last_page":"534"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9901999831199646,"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":0.9901999831199646,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/gaussian-process","display_name":"Gaussian process","score":0.7539297342300415},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.6400650143623352},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.625403881072998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5743535161018372},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5324106216430664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4472004771232605},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4384022057056427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3814328610897064},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38035720586776733},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33104538917541504},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30380523204803467},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0768575668334961}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7539297342300415},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.6400650143623352},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.625403881072998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5743535161018372},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5324106216430664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4472004771232605},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4384022057056427},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3814328610897064},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38035720586776733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33104538917541504},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30380523204803467},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0768575668334961},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap45676.2019.9022469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap45676.2019.9022469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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":20,"referenced_works":["https://openalex.org/W1483365869","https://openalex.org/W1683821609","https://openalex.org/W1775626196","https://openalex.org/W1963375715","https://openalex.org/W2028082528","https://openalex.org/W2104314841","https://openalex.org/W2108755939","https://openalex.org/W2124039338","https://openalex.org/W2165577558","https://openalex.org/W2281833786","https://openalex.org/W2515822248","https://openalex.org/W2750632489","https://openalex.org/W2754974146","https://openalex.org/W2807346250","https://openalex.org/W2890437568","https://openalex.org/W2963086989","https://openalex.org/W4211049957","https://openalex.org/W4289761856","https://openalex.org/W6628808406","https://openalex.org/W6695317373"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W2385263368","https://openalex.org/W2347422947","https://openalex.org/W2061292372","https://openalex.org/W2353392568","https://openalex.org/W2377341410","https://openalex.org/W1454600515","https://openalex.org/W328874995","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Gaussian":[0],"Process":[1],"(GP)":[2],"inference":[3,53,92],"in":[4,11,21,74,88,104,132],"dynamical":[5],"piecewise":[6],"stationary":[7],"environments":[8],"finds":[9],"application":[10],"many":[12],"modern":[13],"real-world":[14],"problems.":[15],"Here":[16],"we":[17,80],"focus":[18],"on":[19,100],"scenarios":[20],"which":[22,43,86],"precise":[23],"initial":[24,72],"training":[25],"data":[26,134,148],"is":[27,33,98],"available":[28],"but":[29],"each":[30],"subsequent":[31],"measurement":[32],"taken":[34],"with":[35,37,90,146],"uncertainty":[36],"respect":[38],"to":[39,163],"the":[40,51,57,66,91,101,115,122,133,141,155],"location":[41],"at":[42],"it":[44],"was":[45],"taken.":[46],"In":[47,77],"such":[48,105],"a":[49,61,82,150],"scenario,":[50],"ideal":[52],"methodology":[54,85],"should":[55,69],"update":[56,67],"GP":[58,123],"only":[59],"if":[60],"considerable":[62],"change":[63,83],"occurs.":[64],"Furthermore,":[65],"strategy":[68,157],"preserve":[70],"\u201ccorrect\u201d":[71],"information":[73],"unchanged":[75],"regions.":[76],"this":[78],"contribution,":[79],"propose":[81],"detection":[84,96,119],"operates":[87],"conjunction":[89],"process.":[93],"The":[94],"proposed":[95,156],"method":[97],"based":[99],"Kolmogorov-Smirnov":[102],"statistics":[103],"way":[106],"that":[107],"an":[108,137],"exact":[109],"PDF":[110],"can":[111,124],"be":[112,125],"derived":[113],"under":[114],"null":[116],"hypothesis.":[117],"Once":[118],"has":[120],"occurred,":[121],"naively":[126],"updated":[127],"by":[128,136],"replacing":[129],"appropriate":[130],"measurements":[131],"set":[135],"average":[138],"computed":[139],"from":[140],"new,":[142],"noisy":[143,164],"samples.":[144],"Simulations":[145],"synthetic":[147],"provide":[149],"proof":[151],"of":[152,154],"concept":[153],"as":[158,160],"well":[159],"its":[161],"robustness":[162],"inputs.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"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"}