{"id":"https://openalex.org/W4387106775","doi":"https://doi.org/10.1007/s11222-023-10294-4","title":"Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers","display_name":"Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers","publication_year":2023,"publication_date":"2023-09-27","ids":{"openalex":"https://openalex.org/W4387106775","doi":"https://doi.org/10.1007/s11222-023-10294-4"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-023-10294-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10294-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10294-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10294-4.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028358733","display_name":"Alessandro Viani","orcid":"https://orcid.org/0000-0001-7469-9828"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Alessandro Viani","raw_affiliation_strings":["Dipartimento di Matematica, Universit\u00e0 di Genova, 16146, Genova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dipartimento di Matematica, Universit\u00e0 di Genova, 16146, Genova, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023235342","display_name":"Adam M. Johansen","orcid":"https://orcid.org/0000-0002-3531-7628"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Adam M. Johansen","raw_affiliation_strings":["Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK"],"raw_orcid":"https://orcid.org/0000-0002-3531-7628","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072847646","display_name":"Alberto Sorrentino","orcid":"https://orcid.org/0000-0003-3457-6780"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alberto Sorrentino","raw_affiliation_strings":["Dipartimento di Matematica, Universit\u00e0 di Genova, 16146, Genova, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dipartimento di Matematica, Universit\u00e0 di Genova, 16146, Genova, Italy","institution_ids":["https://openalex.org/I83816512"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028358733"],"corresponding_institution_ids":["https://openalex.org/I83816512"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":0.6586,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72242418,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"33","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9990000128746033,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9975000023841858,"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/posterior-probability","display_name":"Posterior probability","score":0.6854740381240845},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.6416578888893127},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5787802338600159},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.568363606929779},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.518798291683197},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5033978819847107},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49230706691741943},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.4873546063899994},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.48242267966270447},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.474946528673172},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.46979817748069763},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4631921648979187},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.46271631121635437},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.45179980993270874},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.444812536239624},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4133460223674774},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2937062978744507},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.1256306767463684}],"concepts":[{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6854740381240845},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.6416578888893127},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5787802338600159},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.568363606929779},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.518798291683197},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5033978819847107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49230706691741943},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.4873546063899994},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.48242267966270447},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.474946528673172},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.46979817748069763},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4631921648979187},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46271631121635437},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.45179980993270874},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.444812536239624},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4133460223674774},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2937062978744507},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.1256306767463684},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s11222-023-10294-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10294-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10294-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:179676","is_oa":true,"landing_page_url":null,"pdf_url":"https://wrap.warwick.ac.uk/179676/2/WRAP-cost-free-hyper-parameter-selection-averaging-Bayesian-inverse-problems-vanilla-Rao-Blackwellized-SMC-samplers-Johansen-2023.pdf","source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"},{"id":"pmh:oai:iris.unige.it:11567/1148455","is_oa":false,"landing_page_url":"https://hdl.handle.net/11567/1148455","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"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":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s11222-023-10294-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10294-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10294-4.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1871434800","display_name":null,"funder_award_id":"EP/R034710/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3121983359","display_name":null,"funder_award_id":"EP/T004134/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6636739331","display_name":"Robust, Scalable Sequential Monte Carlo with Application To Urban Air Quality","funder_award_id":"EP/T004134/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G8222295138","display_name":null,"funder_award_id":"EP/R034710/1 and EP/T004134/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G926698369","display_name":null,"funder_award_id":"R034710","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G965103402","display_name":"CoSInES (COmputational Statistical INference for Engineering and Security)","funder_award_id":"EP/R034710/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320322794","display_name":"Universit\u00e0 degli Studi di Genova","ror":"https://ror.org/0107c5v14"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387106775.pdf"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1513873506","https://openalex.org/W1641947403","https://openalex.org/W1986252411","https://openalex.org/W2064902060","https://openalex.org/W2069739265","https://openalex.org/W2079325778","https://openalex.org/W2084333685","https://openalex.org/W2101295974","https://openalex.org/W2130363011","https://openalex.org/W2130416410","https://openalex.org/W2135267747","https://openalex.org/W2136634080","https://openalex.org/W2147357149","https://openalex.org/W2149020252","https://openalex.org/W2149498546","https://openalex.org/W2198904917","https://openalex.org/W2271646078","https://openalex.org/W2318405211","https://openalex.org/W2912102539","https://openalex.org/W3084298117","https://openalex.org/W3090482648","https://openalex.org/W3099114133","https://openalex.org/W3102227782","https://openalex.org/W3105504094","https://openalex.org/W3109346590","https://openalex.org/W3162602167","https://openalex.org/W3214482604"],"related_works":["https://openalex.org/W4388397594","https://openalex.org/W2356093187","https://openalex.org/W164417758","https://openalex.org/W2186819079","https://openalex.org/W4233488282","https://openalex.org/W4287728430","https://openalex.org/W2794631075","https://openalex.org/W3040609822","https://openalex.org/W1991443374","https://openalex.org/W2577958329"],"abstract_inverted_index":{"Abstract":[0],"In":[1,67,189],"Bayesian":[2,166],"inverse":[3],"problems,":[4,22],"one":[5,203],"aims":[6],"at":[7,95,184,196,217],"characterizing":[8],"the":[9,50,53,85,107,133,143,157,172,176,191,198,205,212,232,236,249,267],"posterior":[10,54,125,251,268],"distribution":[11,126,163,269],"of":[12,15,45,84,110,118,132,142,156,179,204,208,270,273],"a":[13,33,57,96,114,124,129,185,238,254,259,271,276],"set":[14],"unknowns,":[16],"given":[17],"indirect":[18],"measurements.":[19],"For":[20,169],"non-linear/non-Gaussian":[21],"analytic":[23],"solutions":[24],"are":[25,220],"seldom":[26],"available:":[27],"Sequential":[28,75],"Monte":[29,76],"Carlo":[30,77],"samplers":[31,79],"offer":[32],"powerful":[34],"tool":[35],"for":[36,60,92,227],"approximating":[37],"complex":[38],"posteriors,":[39],"by":[40,105],"constructing":[41,106],"an":[42,82,242],"auxiliary":[43,108],"sequence":[44,109],"densities":[46],"that":[47,72,116,214],"smoothly":[48],"reaches":[49],"posterior.":[51],"Often":[52],"depends":[55],"on":[56],"scalar":[58],"hyper-parameter,":[59],"which":[61],"limited":[62],"prior":[63,181],"information":[64],"is":[65,245,263],"available.":[66],"this":[68,90],"work,":[69],"we":[70],"show":[71,224],"properly":[73],"designed":[74],"(SMC)":[78],"naturally":[80],"provide":[81],"approximation":[83],"marginal":[86],"likelihood":[87],"associated":[88],"with":[89],"hyper-parameter":[91,144,158,233],"free,":[93],"i.e.":[94,211],"negligible":[97,186],"additional":[98],"computational":[99,187],"cost.":[100,188],"The":[101],"proposed":[102,173,192],"method":[103,174,193],"proceeds":[104],"distributions":[111],"in":[112,145,164,275],"such":[113],"way":[115],"each":[117],"them":[119],"can":[120,136],"be":[121,137],"interpreted":[122],"as":[123,150,152],"corresponding":[127],"to":[128,139,160,247,265],"different":[130],"value":[131],"hyper-parameter.":[134],"This":[135],"exploited":[138],"perform":[140],"selection":[141],"Empirical":[146],"Bayes":[147],"(EB)":[148],"approaches,":[149,171],"well":[151],"averaging":[153],"across":[154],"values":[155],"according":[159],"some":[161],"hyper-prior":[162],"Fully":[165],"(FB)":[167],"approaches.":[168],"FB":[170],"has":[175],"further":[177],"benefit":[178],"allowing":[180],"sensitivity":[182],"analysis":[183],"addition,":[190],"exploits":[194],"particles":[195],"all":[197,215],"(relevant)":[199],"iterations,":[200],"thus":[201],"alleviating":[202],"known":[206],"limitations":[207],"SMC":[209,243,261],"samplers,":[210],"fact":[213],"samples":[216],"intermediate":[218],"iterations":[219],"typically":[221],"discarded.":[222],"We":[223],"numerical":[225],"results":[226],"two":[228],"distinct":[229],"cases":[230],"where":[231,241,258],"affects":[234],"only":[235],"likelihood:":[237],"toy":[239],"example,":[240,257],"sampler":[244,262],"used":[246,264],"approximate":[248,266],"full":[250],"distribution;":[252],"and":[253],"brain":[255],"imaging":[256],"Rao-Blackwellized":[260],"subset":[272],"parameters":[274],"conditionally":[277],"linear":[278],"Gaussian":[279],"model.":[280]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-01-25T23:04:38.658462","created_date":"2025-10-10T00:00:00"}
