{"id":"https://openalex.org/W4226160112","doi":"https://doi.org/10.1007/s00180-023-01345-5","title":"Generative models and Bayesian inversion using Laplace approximation","display_name":"Generative models and Bayesian inversion using Laplace approximation","publication_year":2023,"publication_date":"2023-03-16","ids":{"openalex":"https://openalex.org/W4226160112","doi":"https://doi.org/10.1007/s00180-023-01345-5"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-023-01345-5","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s00180-023-01345-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01345-5.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","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/s00180-023-01345-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011548948","display_name":"Manuel Marschall","orcid":null},"institutions":[{"id":"https://openalex.org/I1285933455","display_name":"Physikalisch-Technische Bundesanstalt","ror":"https://ror.org/05r3f7h03","country_code":"DE","type":"government","lineage":["https://openalex.org/I1285933455","https://openalex.org/I4210136623"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Manuel Marschall","raw_affiliation_strings":["Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0003-0648-1936","affiliations":[{"raw_affiliation_string":"Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany","institution_ids":["https://openalex.org/I1285933455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072530200","display_name":"Gerd W\u00fcbbeler","orcid":"https://orcid.org/0000-0002-6871-8903"},"institutions":[{"id":"https://openalex.org/I1285933455","display_name":"Physikalisch-Technische Bundesanstalt","ror":"https://ror.org/05r3f7h03","country_code":"DE","type":"government","lineage":["https://openalex.org/I1285933455","https://openalex.org/I4210136623"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gerd W\u00fcbbeler","raw_affiliation_strings":["Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0002-6871-8903","affiliations":[{"raw_affiliation_string":"Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany","institution_ids":["https://openalex.org/I1285933455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021156124","display_name":"Franko Schm\u00e4hling","orcid":"https://orcid.org/0000-0001-5081-2742"},"institutions":[{"id":"https://openalex.org/I1285933455","display_name":"Physikalisch-Technische Bundesanstalt","ror":"https://ror.org/05r3f7h03","country_code":"DE","type":"government","lineage":["https://openalex.org/I1285933455","https://openalex.org/I4210136623"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Franko Schm\u00e4hling","raw_affiliation_strings":["Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0001-5081-2742","affiliations":[{"raw_affiliation_string":"Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany","institution_ids":["https://openalex.org/I1285933455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084419307","display_name":"Clemens Elster","orcid":"https://orcid.org/0000-0003-0113-3713"},"institutions":[{"id":"https://openalex.org/I1285933455","display_name":"Physikalisch-Technische Bundesanstalt","ror":"https://ror.org/05r3f7h03","country_code":"DE","type":"government","lineage":["https://openalex.org/I1285933455","https://openalex.org/I4210136623"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Clemens Elster","raw_affiliation_strings":["Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0003-0113-3713","affiliations":[{"raw_affiliation_string":"Physikalisch-Technische Bundesanstalt, Abbestra\u00dfe 2-12, 10587, Berlin, Germany","institution_ids":["https://openalex.org/I1285933455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011548948"],"corresponding_institution_ids":["https://openalex.org/I1285933455"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00161833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":"3","first_page":"1321","last_page":"1349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2596000134944916,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2596000134944916,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.16259999573230743,"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":0.09040000289678574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6677911281585693},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5955371260643005},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5607065558433533},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5370880961418152},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.5242738723754883},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.51957106590271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5150294303894043},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5013515949249268},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47464996576309204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45140206813812256},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4496578872203827},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.4250791370868683},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41570961475372314},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3754235506057739},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3427940905094147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25423184037208557},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.07928147912025452}],"concepts":[{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6677911281585693},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5955371260643005},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5607065558433533},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5370880961418152},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.5242738723754883},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.51957106590271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5150294303894043},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5013515949249268},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47464996576309204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45140206813812256},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4496578872203827},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.4250791370868683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41570961475372314},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3754235506057739},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3427940905094147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25423184037208557},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.07928147912025452}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00180-023-01345-5","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s00180-023-01345-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01345-5.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01345-5","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s00180-023-01345-5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"}],"best_oa_location":{"id":"doi:10.1007/s00180-023-01345-5","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s00180-023-01345-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-023-01345-5.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226160112.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W143236119","https://openalex.org/W1570089119","https://openalex.org/W1963643305","https://openalex.org/W1973768550","https://openalex.org/W1976041275","https://openalex.org/W1983607152","https://openalex.org/W2007339694","https://openalex.org/W2063397409","https://openalex.org/W2077515295","https://openalex.org/W2111609296","https://openalex.org/W2418704535","https://openalex.org/W2502759836","https://openalex.org/W2890139949","https://openalex.org/W2904843110","https://openalex.org/W2908758302","https://openalex.org/W2952020389","https://openalex.org/W2954996726","https://openalex.org/W2963968539","https://openalex.org/W2963971656","https://openalex.org/W2964020599","https://openalex.org/W2964031641","https://openalex.org/W2985931096","https://openalex.org/W2995422623","https://openalex.org/W2996450174","https://openalex.org/W3012096086","https://openalex.org/W3017257212","https://openalex.org/W3030204642","https://openalex.org/W3035574324","https://openalex.org/W3045332022","https://openalex.org/W3134296671","https://openalex.org/W3160913560","https://openalex.org/W3215344152","https://openalex.org/W4206566734","https://openalex.org/W4247690662","https://openalex.org/W4283643017","https://openalex.org/W6600364069","https://openalex.org/W6601643456","https://openalex.org/W6676736541"],"related_works":["https://openalex.org/W10567366","https://openalex.org/W12949870","https://openalex.org/W731497","https://openalex.org/W12314118","https://openalex.org/W7751319","https://openalex.org/W3801212","https://openalex.org/W9869893","https://openalex.org/W3957722","https://openalex.org/W3858182","https://openalex.org/W9895381"],"abstract_inverted_index":{"Abstract":[0],"The":[1,123,255],"Bayesian":[2,43,185],"approach":[3,243,275],"to":[4,24,64,207,241,261],"solving":[5],"inverse":[6,44,90,144],"problems":[7,45],"relies":[8],"on":[9,97,170,188],"the":[10,37,40,77,89,102,105,113,116,129,134,140,143,160,164,171,174,198,210,220,225,242,246,250,274,281,285,296,302],"choice":[11],"of":[12,39,72,80,104,112,142,173,224,249,298,306],"a":[13,28,58,73,85,92,98,150,156,290],"prior.":[14],"This":[15],"critical":[16],"ingredient":[17],"allows":[18],"expert":[19],"knowledge":[20],"or":[21],"physical":[22],"constraints":[23],"be":[25,278],"formulated":[26],"in":[27,61,84,115,119,128,159,197,239,244,284],"probabilistic":[29,189],"fashion":[30],"and":[31,163,289,304],"plays":[32],"an":[33,183],"important":[34],"role":[35],"for":[36,295],"success":[38],"inference.":[41],"Recently,":[42],"were":[46],"solved":[47],"using":[48],"generative":[49,93,135,175,190,221,251],"models":[50,56],"as":[51,109],"highly":[52],"informative":[53],"priors.":[54],"Generative":[55],"are":[57,228,237,309],"popular":[59],"tool":[60],"machine":[62],"learning":[63],"generate":[65],"data":[66,81,257,286],"whose":[67],"properties":[68,103],"closely":[69],"resemble":[70],"those":[71],"given":[74],"database.":[75],"Typically,":[76],"generated":[78],"distribution":[79],"is":[82,95,125,194,205,217,253,259,271,287,293],"embedded":[83],"low-dimensional":[86,130,247],"manifold.":[87],"For":[88,177],"problem,":[91],"model":[94,136,252],"trained":[96],"database":[99],"that":[100,137,152,233,265,273],"reflects":[101],"sought":[106],"solution,":[107],"such":[108],"typical":[110],"structures":[111],"tissue":[114],"human":[117],"brain":[118],"magnetic":[120],"resonance":[121],"imaging.":[122],"inference":[124,186,193,227],"carried":[126,195],"out":[127,196],"manifold":[131,248],"determined":[132],"by":[133,219],"strongly":[138,168],"reduces":[139],"dimensionality":[141],"problem.":[145],"However,":[146],"this":[147,192,299],"procedure":[148],"produces":[149],"posterior":[151],"does":[153],"not":[154],"admit":[155],"Lebesgue":[157],"density":[158,213],"actual":[161],"variables":[162],"accuracy":[165],"attained":[166],"can":[167,277],"depend":[169],"quality":[172],"model.":[176,222],"linear":[178],"Gaussian":[179],"models,":[180],"we":[181,231],"explore":[182],"alternative":[184],"based":[187],"models;":[191],"original":[199],"high-dimensional":[200],"space.":[201],"A":[202],"Laplace":[203],"approximation":[204],"employed":[206],"analytically":[208],"derive":[209],"prior":[211],"probability":[212],"function":[214],"required,":[215],"which":[216,245],"induced":[218],"Properties":[223],"resulting":[226],"investigated.":[229],"Specifically,":[230],"show":[232],"derived":[234],"Bayes":[235],"estimates":[236],"consistent,":[238],"contrast":[240],"employed.":[254],"MNIST":[256],"set":[258],"used":[260],"design":[262],"numerical":[263],"experiments":[264],"confirm":[266],"our":[267],"theoretical":[268],"findings.":[269],"It":[270],"shown":[272],"proposed":[276],"advantageous":[279],"when":[280],"information":[282],"contained":[283],"high":[288],"simple":[291],"heuristic":[292],"considered":[294],"detection":[297],"case.":[300],"Finally,":[301],"pros":[303],"cons":[305],"both":[307],"approaches":[308],"discussed.":[310]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2022-05-05T00:00:00"}
