{"id":"https://openalex.org/W2997945624","doi":null,"title":"Superfast Inference for Stationary Gaussian Processes in Particle Tracking Microrheology","display_name":"Superfast Inference for Stationary Gaussian Processes in Particle Tracking Microrheology","publication_year":2019,"publication_date":"2019-12-18","ids":{"openalex":"https://openalex.org/W2997945624","mag":"2997945624"},"language":"en","primary_location":{"id":"pmh:oai:uwspace.uwaterloo.ca:10012/15338","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/15338","pdf_url":"http://hdl.handle.net/10012/15338","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Doctoral Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10012/15338","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038947417","display_name":"Yun Ling","orcid":"https://orcid.org/0000-0001-8512-1079","observed_orcids":["https://orcid.org/0000-0001-8512-1079"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yun Ling","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8512-1079","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5038947417"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.1680999994277954,"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":0.1680999994277954,"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/T10288","display_name":"Fractional Differential Equations Solutions","score":0.1348000019788742,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11379","display_name":"Rheology and Fluid Dynamics Studies","score":0.10409999638795853,"subfield":{"id":"https://openalex.org/subfields/1507","display_name":"Fluid Flow and Transfer Processes"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-tracking-microrheology","display_name":"particle tracking microrheology","score":0.9129999876022339},{"id":"https://openalex.org/keywords/stationary-gaussian-processes","display_name":"stationary Gaussian processes","score":0.9700000286102295},{"id":"https://openalex.org/keywords/parametric-inference","display_name":"parametric inference","score":0.8119999766349792},{"id":"https://openalex.org/keywords/high-frequency-noise-filtering","display_name":"high-frequency noise filtering","score":0.8370000123977661},{"id":"https://openalex.org/keywords/single-particle-tracking","display_name":"single-particle tracking","score":0.640999972820282},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.47699999809265137},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"frequentist inference","score":0.7089999914169312}],"concepts":[{"id":"https://openalex.org/C34123561","wikidata":"https://www.wikidata.org/wiki/Q6840000","display_name":"Microrheology","level":3,"score":0.9206209778785706},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6264699101448059},{"id":"https://openalex.org/C2778517922","wikidata":"https://www.wikidata.org/wiki/Q7140482","display_name":"Particle (ecology)","level":2,"score":0.4901362359523773},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.47127100825309753},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.45811882615089417},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4575963616371155},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4476524591445923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4364805817604065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33633488416671753},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3148036003112793},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1878509521484375},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13888350129127502},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.09177535772323608},{"id":"https://openalex.org/C186541917","wikidata":"https://www.wikidata.org/wiki/Q910483","display_name":"Viscoelasticity","level":2,"score":0.06351613998413086},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0600036084651947},{"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/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:uwspace.uwaterloo.ca:10012/15338","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/15338","pdf_url":"http://hdl.handle.net/10012/15338","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Doctoral Thesis"},{"id":"mag:2997945624","doi":null,"is_oa":false,"landing_page_url":"https://uwspace.uwaterloo.ca/bitstream/10012/15338/1/Ling_Yun.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:uwspace.uwaterloo.ca:10012/15338","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10012/15338","pdf_url":"http://hdl.handle.net/10012/15338","source":{"id":"https://openalex.org/S4306401661","display_name":"UWSpace (University of Waterloo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I151746483","host_organization_name":"University of Waterloo","host_organization_lineage":["https://openalex.org/I151746483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Doctoral Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2997945624.pdf","grobid_xml":"https://content.openalex.org/works/W2997945624.grobid-xml"},"referenced_works_count":93,"referenced_works":["https://openalex.org/W59552121","https://openalex.org/W165370603","https://openalex.org/W210359992","https://openalex.org/W212854950","https://openalex.org/W248498076","https://openalex.org/W1510052597","https://openalex.org/W1522512418","https://openalex.org/W1528659953","https://openalex.org/W1560584359","https://openalex.org/W1593828278","https://openalex.org/W1598072106","https://openalex.org/W1654787807","https://openalex.org/W1746819321","https://openalex.org/W1923274812","https://openalex.org/W1969243694","https://openalex.org/W1973915465","https://openalex.org/W1974642741","https://openalex.org/W1974949279","https://openalex.org/W1976962685","https://openalex.org/W1977805552","https://openalex.org/W1980763573","https://openalex.org/W1984869715","https://openalex.org/W1988147327","https://openalex.org/W1992216266","https://openalex.org/W1993327079","https://openalex.org/W1996736088","https://openalex.org/W2000583785","https://openalex.org/W2001224529","https://openalex.org/W2010355946","https://openalex.org/W2010739090","https://openalex.org/W2010961009","https://openalex.org/W2011586511","https://openalex.org/W2016533177","https://openalex.org/W2018502278","https://openalex.org/W2021760340","https://openalex.org/W2029924743","https://openalex.org/W2030597423","https://openalex.org/W2033791616","https://openalex.org/W2034161641","https://openalex.org/W2041165203","https://openalex.org/W2042629182","https://openalex.org/W2050622140","https://openalex.org/W2054625522","https://openalex.org/W2054821964","https://openalex.org/W2055124272","https://openalex.org/W2055781590","https://openalex.org/W2058241222","https://openalex.org/W2058597907","https://openalex.org/W2061040818","https://openalex.org/W2067678611","https://openalex.org/W2069677963","https://openalex.org/W2070760846","https://openalex.org/W2075070043","https://openalex.org/W2076115100","https://openalex.org/W2079321251","https://openalex.org/W2083095625","https://openalex.org/W2083930221","https://openalex.org/W2085352155","https://openalex.org/W2085792948","https://openalex.org/W2087034140","https://openalex.org/W2097711273","https://openalex.org/W2099051954","https://openalex.org/W2102002215","https://openalex.org/W2105591391","https://openalex.org/W2107932713","https://openalex.org/W2112364348","https://openalex.org/W2114336941","https://openalex.org/W2114738186","https://openalex.org/W2117333931","https://openalex.org/W2123147311","https://openalex.org/W2123687908","https://openalex.org/W2135622428","https://openalex.org/W2136105491","https://openalex.org/W2140971281","https://openalex.org/W2145517691","https://openalex.org/W2153917500","https://openalex.org/W2154855352","https://openalex.org/W2156974606","https://openalex.org/W2157380881","https://openalex.org/W2158958710","https://openalex.org/W2164685036","https://openalex.org/W2183928100","https://openalex.org/W2303032244","https://openalex.org/W2321676322","https://openalex.org/W2417956311","https://openalex.org/W2480681609","https://openalex.org/W2552605897","https://openalex.org/W2615256066","https://openalex.org/W2754170747","https://openalex.org/W2788099024","https://openalex.org/W2798056406","https://openalex.org/W2888107234","https://openalex.org/W3022909171"],"related_works":["https://openalex.org/W2338628597","https://openalex.org/W2081455211","https://openalex.org/W4286585499","https://openalex.org/W2790015441","https://openalex.org/W2417956311","https://openalex.org/W2357139794","https://openalex.org/W2974921312","https://openalex.org/W2092508882","https://openalex.org/W2318733182","https://openalex.org/W3203701255"],"abstract_inverted_index":{"Particle":[0],"tracking":[1,198,301],"of":[2,11,39,46,62,80,85,91,104,120,137,174,213,243,256,267,271,294,319,321],"passive":[3],"microscopic":[4],"species":[5],"has":[6],"become":[7],"the":[8,18,24,35,44,89,92,135,186,291,317,334,337],"experimental":[9,273],"measurement":[10],"choice":[12],"in":[13,70,109,134,195,226,298],"diverse":[14],"applications,":[15],"where":[16],"either":[17],"material":[19],"volumes":[20],"are":[21,27,207,248,303],"limited,":[22],"or":[23],"materials":[25],"themselves":[26],"so":[28],"soft":[29],"that":[30,203,278,336],"they":[31],"deform":[32],"uncontrollably":[33],"under":[34],"stresses":[36],"and":[37,49,73,151,158,176,182,260,274,330],"strains":[38],"traditional":[40,127],"instruments.":[41],"As":[42],"such,":[43],"results":[45],"countless":[47],"biological":[48,296],"rheological":[50],"analyses":[51],"hinge":[52],"pivotally":[53],"on":[54],"extracting":[55],"reliable":[56],"dynamical":[57],"information":[58],"from":[59,241],"large":[60],"datasets":[61,276],"particle":[63,115,197,204,244,300],"trajectory":[64,116],"recordings.":[65],"However,":[66],"to":[67,189,217,223,237,251,262,315],"do":[68],"this":[69],"a":[71,78,101,141,171,234,253,310,322,326],"statistically":[72],"computationally":[74,327],"efficient":[75,264,328],"manner":[76],"presents":[77],"number":[79,136],"important":[81],"challenges.":[82],"Addressing":[83],"some":[84],"these":[86,220],"challenges":[87],"is":[88,202,290,339,343],"focus":[90],"present":[93,100,140],"work.":[94],"\\n":[95],"\\nIn":[96,179],"Chapter":[97,230,306],"2,":[98],"we":[99,184,232,288,308],"superfast":[102,142,154,164,187],"set":[103],"tools":[105],"for":[106,114,125,144,147,156,166,219,349],"parametric":[107,145],"inference":[108,129,146,165],"single-particle":[110],"tracking.":[111],"Parametric":[112],"likelihoods":[113],"measurements":[117,206,242],"typically":[118],"consist":[119],"stationary":[121,148,167],"Gaussian":[122,149,168],"time":[123],"series,":[124],"which":[126,299,314],"fast":[128],"algorithms":[130,155],"scale":[131],"as":[132],"N-square":[133],"observations.":[138],"We":[139],"algorithm":[143],"processes":[150],"propose":[152,233,309],"novel":[153,235],"score":[157],"Hessian":[159],"calculations.":[160],"This":[161],"effectively":[162],"enables":[163],"process":[169],"via":[170],"wide":[172],"array":[173],"frequentist":[175],"Bayesian":[177],"methods.":[178],"Chapters":[180],"3":[181,231],"4,":[183,307],"use":[185],"toolkit":[188],"address":[190,289],"two":[191],"outstanding":[192],"problems":[193],"prevalent":[194],"many":[196],"analyses.":[199],"The":[200,341],"first":[201],"position":[205],"generally":[208],"contaminated":[209],"by":[210,313,345],"various":[211],"forms":[212],"high-frequency":[214,239,257],"errors.":[215],"Failure":[216],"account":[218],"errors":[221],"leads":[222],"considerable":[224,292],"bias":[225],"estimation":[227],"results.":[228],"In":[229,305],"strategy":[236],"filter":[238],"noise":[240,258],"positions.":[245],"Our":[246],"filters":[247],"shown":[249],"theoretically":[250],"cover":[252],"vast":[254],"range":[255],"regimes":[259],"lead":[261],"an":[263],"computational":[265],"estimator":[266,329],"model":[268],"coefficients.":[269],"Analyses":[270],"numerous":[272],"simulated":[275],"suggest":[277],"our":[279],"filtering":[280],"approach":[281],"performs":[282],"remarkably":[283],"well.":[284],"\\nThe":[285],"second":[286],"problem":[287],"heterogeneity":[293,320],"typical":[295],"fluids":[297],"experiments":[302],"conducted.":[304],"simple":[311],"metric":[312],"quantify":[316],"degree":[318],"fluid,":[323],"along":[324],"with":[325],"statistical":[331],"test":[332],"against":[333],"hypothesis":[335],"fluid":[338],"homogeneous.":[340],"thesis":[342],"concluded":[344],"outlining":[346],"several":[347],"directions":[348],"future":[350],"research.":[351]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-01-10T00:00:00"}