{"id":"https://openalex.org/W7161716703","doi":"https://doi.org/10.48550/arxiv.2605.16644","title":"The Score Kalman Filter","display_name":"The Score Kalman Filter","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161716703","doi":"https://doi.org/10.48550/arxiv.2605.16644"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16644","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16644","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136468937","display_name":"Kaito Iwasaki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iwasaki, Kaito","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136468424","display_name":"Anthony Bloch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bloch, Anthony","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020132536","display_name":"Taeyoung Lee","orcid":"https://orcid.org/0000-0003-4982-4150"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Taeyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046777734","display_name":"Maani Ghaffari","orcid":"https://orcid.org/0000-0002-4734-4295"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghaffari, Maani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"issue":null,"first_page":null,"last_page":null},"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":0.8363999724388123,"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":0.8363999724388123,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.04490000009536743,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.023399999365210533,"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/kalman-filter","display_name":"Kalman filter","score":0.5608000159263611},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4684999883174896},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44350001215934753},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4323999881744385},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.38260000944137573},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.3808000087738037},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.35749998688697815}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6791999936103821},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48539999127388},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4323999881744385},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.38260000944137573},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.35749998688697815},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.3285999894142151},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32589998841285706},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C8639503","wikidata":"https://www.wikidata.org/wiki/Q6059511","display_name":"Invariant extended Kalman filter","level":4,"score":0.296999990940094},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C139722471","wikidata":"https://www.wikidata.org/wiki/Q2619517","display_name":"Linear filter","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.27720001339912415},{"id":"https://openalex.org/C2778401447","wikidata":"https://www.wikidata.org/wiki/Q7140637","display_name":"Partition function (quantum field theory)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2632000148296356},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C137685913","wikidata":"https://www.wikidata.org/wiki/Q4316763","display_name":"Nonlinear filter","level":4,"score":0.25999999046325684},{"id":"https://openalex.org/C101796028","wikidata":"https://www.wikidata.org/wiki/Q535587","display_name":"Moment-generating function","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.25529998540878296},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16644","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16644","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"central":[1],"obstacle":[2],"in":[3],"nonlinear":[4,158],"Bayesian":[5,121],"filtering":[6,59],"is":[7],"representing":[8],"the":[9,29,33,41,55,66,83,97,109,124,141,161,171,179],"belief":[10],"distribution.":[11],"Moment-based":[12],"filters":[13],"address":[14],"this":[15],"by":[16,70],"propagating":[17],"polynomial":[18],"moments":[19,118],"and":[20,44,114,150,166,175],"reconstructing":[21],"a":[22,87,147],"density":[23,84],"from":[24,96],"them.":[25],"Recent":[26],"work":[27],"completes":[28],"predict-update":[30,126],"loop":[31,127],"via":[32],"maximum-entropy":[34],"(MaxEnt)":[35],"principle,":[36],"but":[37],"each":[38,120],"step":[39,153],"requires":[40],"partition":[42,67,130],"function":[43,68,131],"its":[45],"gradient,":[46],"both":[47],"$n$-dimensional":[48],"integrals":[49],"whose":[50,91],"cost":[51],"scales":[52],"exponentially,":[53],"restricting":[54],"demonstrated":[56],"MaxEnt":[57],"moment":[58,110],"to":[60,86,107,115,140],"$n":[61],"\\le":[62],"4$.":[63],"We":[64],"avoid":[65],"entirely":[69],"combining":[71],"score":[72,80],"matching":[73,81],"with":[74],"Stein's":[75,105],"identity.":[76],"In":[77],"our":[78],"setting,":[79],"reduces":[82,139],"fit":[85],"single":[88],"linear":[89,155],"solve":[90],"coefficients":[92],"are":[93],"assembled":[94],"directly":[95],"propagated":[98],"moments.":[99],"The":[100,133],"same":[101],"parameters":[102],"then":[103],"drive":[104],"identity":[106],"close":[108],"hierarchy":[111],"during":[112],"prediction":[113],"recover":[116],"posterior":[117],"after":[119],"update,":[122],"keeping":[123],"full":[125],"free":[128],"of":[129],"evaluation.":[132],"resulting":[134],"Score":[135],"Kalman":[136,144],"Filter":[137],"(SKF)":[138],"classical":[142],"information-form":[143],"filter":[145],"as":[146],"special":[148],"case":[149],"performs":[151],"every":[152],"through":[154,164],"algebra.":[156],"On":[157],"coupled-oscillator":[159],"networks,":[160],"SKF":[162],"runs":[163],"$n=20$":[165],"reports":[167],"lower":[168],"RMSE":[169],"than":[170],"EKF,":[172],"UKF,":[173],"EnKF,":[174],"particle-filter":[176],"baselines":[177],"on":[178],"tested":[180],"synthetic":[181],"benchmarks.":[182]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-20T00:00:00"}
