{"id":"https://openalex.org/W2888774043","doi":"https://doi.org/10.1109/ssp.2018.8450780","title":"Misspecified Bayesian Cram\u00e9r-Rao Bound for Sparse Bayesian","display_name":"Misspecified Bayesian Cram\u00e9r-Rao Bound for Sparse Bayesian","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2888774043","doi":"https://doi.org/10.1109/ssp.2018.8450780","mag":"2888774043"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450780","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","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/A5042113276","display_name":"Milutin Pajovic","orcid":"https://orcid.org/0000-0001-5033-017X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Milutin Pajovic","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5042113276"],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":0.3303,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.55778028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"263","last_page":"267"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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":1.0,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9995999932289124,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.7060750722885132},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6938379406929016},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5372018814086914},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5281633734703064},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5018916130065918},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4789438843727112},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4769805371761322},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4571426510810852},{"id":"https://openalex.org/keywords/bayesian-average","display_name":"Bayesian average","score":0.43052399158477783},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38427767157554626},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3726744055747986},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.3511016070842743},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3220575451850891},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.30996447801589966},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.22932091355323792}],"concepts":[{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.7060750722885132},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6938379406929016},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5372018814086914},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5281633734703064},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5018916130065918},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4789438843727112},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4769805371761322},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4571426510810852},{"id":"https://openalex.org/C149569020","wikidata":"https://www.wikidata.org/wiki/Q25098598","display_name":"Bayesian average","level":5,"score":0.43052399158477783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38427767157554626},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3726744055747986},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.3511016070842743},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3220575451850891},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.30996447801589966},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.22932091355323792},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450780","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450780","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (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":12,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1523831565","https://openalex.org/W1976709621","https://openalex.org/W1979671221","https://openalex.org/W2116148865","https://openalex.org/W2135046866","https://openalex.org/W2402526535","https://openalex.org/W2510604076","https://openalex.org/W2759547991","https://openalex.org/W3100410537","https://openalex.org/W4285719527","https://openalex.org/W4289784446"],"related_works":["https://openalex.org/W4235165088","https://openalex.org/W2753218748","https://openalex.org/W3002319139","https://openalex.org/W4328114192","https://openalex.org/W2889562828","https://openalex.org/W2953280030","https://openalex.org/W1573215448","https://openalex.org/W1980584819","https://openalex.org/W4300815303","https://openalex.org/W2951176680"],"abstract_inverted_index":{"We":[0],"consider":[1],"a":[2,9,31,54,59,70,74,80],"misspecified":[3,75],"Bayesian":[4,34,64,71],"Cram\u00e9r-Raobound":[5],"(MBCRB),":[6],"justified":[7],"in":[8],"scenario":[10],"where":[11,38],"the":[12,19,39,45,63,67,84,91,99,105,109],"assumed":[13,40],"data":[14,41,76],"model":[15],"is":[16,48],"different":[17,43],"from":[18,44],"true":[20,46],"generative":[21],"model.":[22],"As":[23],"an":[24],"example":[25],"of":[26,58],"this":[27],"scenario,":[28],"we":[29,78],"study":[30,97],"popular":[32],"sparse":[33,60,93],"learning":[35],"(SBL)":[36],"algorithm":[37],"model,":[42,47,77],"constructed":[49],"so":[50],"as":[51,69],"to":[52,90],"facilitate":[53],"computationally":[55],"feasible":[56],"inference":[57,72],"signal":[61],"within":[62],"framework.":[65],"Formulating":[66],"SBL":[68,106],"with":[73],"derive":[79],"lower":[81],"bound":[82,101],"on":[83],"mean":[85],"square":[86],"error":[87],"(MSE)":[88],"corresponding":[89],"estimated":[92],"signal.":[94],"The":[95],"simulation":[96],"validates":[98],"derived":[100],"and":[102],"shows":[103],"that":[104],"performance":[107],"approaches":[108],"MBCRB":[110],"at":[111],"very":[112],"high":[113],"signal-to-noise":[114],"ratios.":[115]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"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"}
