{"id":"https://openalex.org/W1985791204","doi":"https://doi.org/10.1109/icassp.2010.5495335","title":"A minimax approach to Bayesian estimation with partial knowledge of the observation model","display_name":"A minimax approach to Bayesian estimation with partial knowledge of the observation model","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W1985791204","doi":"https://doi.org/10.1109/icassp.2010.5495335","mag":"1985791204"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2010.5495335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2010.5495335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","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/A5058178036","display_name":"Tomer Michaeli","orcid":"https://orcid.org/0000-0003-0525-8054"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Tomer Michaeli","raw_affiliation_strings":["Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel","Department of Electrical Engineering, Technion??Israel Institute of Technology, Haifa, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"Department of Electrical Engineering, Technion??Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005913897","display_name":"Yonina C. Eldar","orcid":"https://orcid.org/0000-0003-4358-5304"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yonina C. Eldar","raw_affiliation_strings":["Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel","Department of Electrical Engineering, Technion??Israel Institute of Technology, Haifa, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"Department of Electrical Engineering, Technion??Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I174306211"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"373","issue":null,"first_page":"1902","last_page":"1905"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9972000122070312,"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":0.9972000122070312,"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":0.9927999973297119,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9860000014305115,"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/estimator","display_name":"Estimator","score":0.6678361892700195},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.5988463163375854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.583922266960144},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5327185392379761},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.527998685836792},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5067387223243713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45529675483703613},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44102734327316284},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.42555347084999084},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.4116142988204956},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.41033002734184265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3748170733451843},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3710761070251465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3321343660354614},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3215946555137634},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1910240352153778}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6678361892700195},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.5988463163375854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.583922266960144},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5327185392379761},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.527998685836792},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5067387223243713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45529675483703613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44102734327316284},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.42555347084999084},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.4116142988204956},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.41033002734184265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3748170733451843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3710761070251465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3321343660354614},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3215946555137634},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1910240352153778},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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":2,"locations":[{"id":"doi:10.1109/icassp.2010.5495335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2010.5495335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.386.5675","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.5675","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://webee.technion.ac.il/people/tomermic/papers/MinimaxPartialKnowledge.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1607735679","https://openalex.org/W1971713783","https://openalex.org/W2106488367","https://openalex.org/W2107467870","https://openalex.org/W2110904776","https://openalex.org/W2138451337","https://openalex.org/W2211925278","https://openalex.org/W2994340921","https://openalex.org/W4231152566","https://openalex.org/W6635552349","https://openalex.org/W6675974536","https://openalex.org/W6688384279"],"related_works":["https://openalex.org/W2526245987","https://openalex.org/W4378551395","https://openalex.org/W779566600","https://openalex.org/W4308064958","https://openalex.org/W3217767640","https://openalex.org/W4226056425","https://openalex.org/W173377860","https://openalex.org/W4230526720","https://openalex.org/W1607663644","https://openalex.org/W4288088475"],"abstract_inverted_index":{"We":[0,20,100,114],"address":[1],"the":[2,8,12,39,42,46,49,54,59,70,79,93,135],"problem":[3],"of":[4,38,45,58,72,127,137,139],"Bayesian":[5],"estimation":[6],"where":[7],"statistical":[9],"relation":[10],"between":[11],"signal":[13,60],"and":[14,41,44,48,61,88],"measurements":[15,62],"is":[16,63],"only":[17],"partially":[18],"known.":[19,52],"propose":[21,116],"modeling":[22],"partial":[23],"Baysian":[24],"knowledge":[25],"by":[26,102],"using":[27],"an":[28,104,145],"auxiliary":[29],"random":[30],"vector":[31],"called":[32],"instrument.":[33],"The":[34],"joint":[35,55],"probability":[36,56],"distributions":[37],"instrument":[40,47,80],"signal,":[43],"measurements,":[50],"are":[51],"However,":[53],"function":[57],"unknown.":[64],"Our":[65],"model":[66],"generalizes":[67],"that":[68,78,142],"underlying":[69],"method":[71,119],"instrumental":[73],"variables":[74],"from":[75,124],"statistics,":[76],"in":[77,134],"does":[81],"not":[82],"have":[83,143],"to":[84,97],"satisfy":[85],"any":[86],"requirements":[87],"no":[89],"parametric":[90],"form":[91],"for":[92,106,120],"optimal":[94],"regressor":[95],"needs":[96],"be":[98],"available.":[99],"begin":[101],"deriving":[103],"estimator":[105,123],"this":[107,122],"scenario,":[108],"via":[109],"a":[110,117,125],"worst-case":[111],"design":[112],"strategy.":[113],"then":[115],"non-parametric":[118],"learning":[121],"set":[126],"examples.":[128],"Finally,":[129],"we":[130],"demonstrate":[131],"our":[132],"approach":[133],"context":[136],"enhancement":[138],"facial":[140],"images":[141],"undergone":[144],"unknown":[146],"degradation.":[147]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
