{"id":"https://openalex.org/W3093495232","doi":"https://doi.org/10.3390/e1040069","title":"A Bayesian Reflection on Surfaces","display_name":"A Bayesian Reflection on Surfaces","publication_year":1999,"publication_date":"1999-10-30","ids":{"openalex":"https://openalex.org/W3093495232","doi":"https://doi.org/10.3390/e1040069","mag":"3093495232"},"language":"en","primary_location":{"id":"doi:10.3390/e1040069","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e1040069","pdf_url":"https://www.mdpi.com/1099-4300/1/4/69/pdf?version=1424784621","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/1/4/69/pdf?version=1424784621","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047752317","display_name":"David R. Wolf","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"David  R. Wolf","raw_affiliation_strings":["PO Box 8308, Austin, TX 78713-8308, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PO Box 8308, Austin, TX 78713-8308, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5047752317"],"corresponding_institution_ids":[],"apc_list":{"value":2600,"currency":"CHF","value_usd":3137},"apc_paid":{"value":2600,"currency":"CHF","value_usd":3137},"fwci":0.5463,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.80233899,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"1","issue":"4","first_page":"69","last_page":"98"},"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":0.9681000113487244,"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":0.9681000113487244,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11698","display_name":"Underwater Acoustics Research","score":0.9495999813079834,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.7236261963844299},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6717925667762756},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5762690901756287},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5300455093383789},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5006198883056641},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.49141061305999756},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.48533859848976135},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4814852476119995},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4721403419971466},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.4718710780143738},{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.4676472842693329},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44034188985824585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4267706573009491},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3775075078010559},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.1776958703994751}],"concepts":[{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.7236261963844299},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6717925667762756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5762690901756287},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5300455093383789},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5006198883056641},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.49141061305999756},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48533859848976135},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4814852476119995},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4721403419971466},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.4718710780143738},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.4676472842693329},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44034188985824585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4267706573009491},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3775075078010559},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.1776958703994751},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/e1040069","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e1040069","pdf_url":"https://www.mdpi.com/1099-4300/1/4/69/pdf?version=1424784621","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:cs/0005027","is_oa":true,"landing_page_url":"http://arxiv.org/abs/cs/0005027","pdf_url":"https://arxiv.org/pdf/cs/0005027","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:464daf162cde4e5b94f8015530c527e6","is_oa":true,"landing_page_url":"https://doaj.org/article/464daf162cde4e5b94f8015530c527e6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 1, Iss 4, Pp 69-98 (1999)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/e1040069","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e1040069","pdf_url":"https://www.mdpi.com/1099-4300/1/4/69/pdf?version=1424784621","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3093495232.pdf","grobid_xml":"https://content.openalex.org/works/W3093495232.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W1581159451","https://openalex.org/W2020999234","https://openalex.org/W2952108606","https://openalex.org/W3089223600"],"related_works":["https://openalex.org/W2944091050","https://openalex.org/W2998817056","https://openalex.org/W2077878098","https://openalex.org/W2904258669","https://openalex.org/W2096986186","https://openalex.org/W4313815718","https://openalex.org/W1506744765","https://openalex.org/W996380913","https://openalex.org/W2024084279","https://openalex.org/W2920185967"],"abstract_inverted_index":{"The":[0,22,93,110],"topic":[1],"of":[2,26,53,57,69,77,117,142],"this":[3,16],"paper":[4,17],"is":[5,30,37,84,99,152],"a":[6,39,46,74],"novel":[7],"Bayesian":[8],"continuous-basis":[9,27],"field":[10,36,96,118],"representation":[11,54],"and":[12,42,60,104,126],"inference":[13,25,97,141],"framework.":[14],"Within":[15],"several":[18],"problems":[19],"are":[20],"solved:":[21],"maximally":[23,94],"informative":[24,95],"fields,":[28],"that":[29,73],"where":[31],"the":[32,35,49,67,80,106,115,140],"basis":[33],"for":[34,90],"itself":[38],"continuous":[40,143],"object":[41,81],"not":[43],"representable":[44],"in":[45,55,65,101],"finite":[47],"manner;":[48],"tradeoff":[50],"between":[51],"accuracy":[52],"terms":[56],"information":[58,78,87],"learned,":[59],"memory":[61],"or":[62],"storage":[63],"capacity":[64],"bits;":[66],"approximation":[68],"probability":[70],"distributions":[71],"so":[72],"maximal":[75],"amount":[76],"about":[79],"being":[82],"inferred":[83],"preserved;":[85],"an":[86],"theoretic":[88],"justification":[89],"multigrid":[91],"methodology.":[92],"framework":[98],"described":[100],"full":[102],"generality":[103],"denoted":[105],"Generalized":[107,111],"Kalman":[108,112],"Filter.":[109],"Filter":[113],"allows":[114],"update":[116],"knowledge":[119,122,131],"from":[120,145],"previous":[121],"at":[123,132],"any":[124,133],"scale,":[125],"new":[127,130],"data,":[128],"to":[129],"other":[134],"scale.":[135],"An":[136],"application":[137],"example":[138],"instance,":[139],"surfaces":[144],"measurements":[146],"(for":[147],"example,":[148],"camera":[149],"image":[150],"data),":[151],"presented.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}