{"id":"https://openalex.org/W3114884557","doi":null,"title":"Score Matched Conditional Exponential Families for Likelihood-Free Inference","display_name":"Score Matched Conditional Exponential Families for Likelihood-Free Inference","publication_year":2020,"publication_date":"2020-12-20","ids":{"openalex":"https://openalex.org/W3114884557","mag":"3114884557"},"language":"en","primary_location":{"id":"mag:3114884557","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2012.10903v1","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2012.10903v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018689327","display_name":"Lorenzo Pacchiardi","orcid":"https://orcid.org/0000-0003-4760-7638","observed_orcids":["https://orcid.org/0000-0003-4760-7638"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lorenzo Pacchiardi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4760-7638","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060445865","display_name":"Ritabrata Dutta","orcid":"https://orcid.org/0000-0001-8209-7747","observed_orcids":["https://orcid.org/0000-0001-8209-7747"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ritabrata Dutta","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8209-7747","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9990000128746033,"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":0.9990000128746033,"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":0.00039999998989515007,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":9.999999747378752e-05,"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/simulation-based-inference","display_name":"simulation-based inference","score":0.9959999918937683},{"id":"https://openalex.org/keywords/score-matching","display_name":"score matching","score":0.7009999752044678},{"id":"https://openalex.org/keywords/approximate-bayesian-computation","display_name":"approximate Bayesian computation","score":0.7649999856948853},{"id":"https://openalex.org/keywords/sufficient-statistics","display_name":"sufficient statistics","score":0.5509999990463257},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6060000061988831}],"concepts":[{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.8521591424942017},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.8338174223899841},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6203204393386841},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5848175287246704},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.5322615504264832},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.49238431453704834},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48990005254745483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47760507464408875},{"id":"https://openalex.org/C61420037","wikidata":"https://www.wikidata.org/wiki/Q7316301","display_name":"Restricted maximum likelihood","level":3,"score":0.44999372959136963},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.44599416851997375},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4442245364189148},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.410697877407074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.410469651222229},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3915386199951172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3912546634674072},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36639776825904846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3022221326828003},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.29958707094192505}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3114884557","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2012.10903v1","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3114884557","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2012.10903v1","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W112870199","https://openalex.org/W1505878979","https://openalex.org/W1565709818","https://openalex.org/W1587348082","https://openalex.org/W1680396847","https://openalex.org/W1793259860","https://openalex.org/W1959736062","https://openalex.org/W1964607942","https://openalex.org/W1970391837","https://openalex.org/W1978556363","https://openalex.org/W1979969656","https://openalex.org/W1984048068","https://openalex.org/W1994129134","https://openalex.org/W2013035813","https://openalex.org/W2025341678","https://openalex.org/W2045973738","https://openalex.org/W2051588269","https://openalex.org/W2052329135","https://openalex.org/W2062125287","https://openalex.org/W2092124742","https://openalex.org/W2116064496","https://openalex.org/W2121794292","https://openalex.org/W2123368367","https://openalex.org/W2135267747","https://openalex.org/W2138204974","https://openalex.org/W2139812092","https://openalex.org/W2152246075","https://openalex.org/W2511837229","https://openalex.org/W2534708821","https://openalex.org/W2577537660","https://openalex.org/W2581158580","https://openalex.org/W2593094133","https://openalex.org/W2626930952","https://openalex.org/W2753890080","https://openalex.org/W2764266478","https://openalex.org/W2804609220","https://openalex.org/W2903442788","https://openalex.org/W2940659020","https://openalex.org/W2949117887","https://openalex.org/W2963295938","https://openalex.org/W2963399751","https://openalex.org/W2964250089","https://openalex.org/W2969696053","https://openalex.org/W2970971581","https://openalex.org/W2975024646","https://openalex.org/W2976226557","https://openalex.org/W2978753741","https://openalex.org/W3086988147","https://openalex.org/W3215415712"],"related_works":["https://openalex.org/W3207538905","https://openalex.org/W4402685570","https://openalex.org/W2900947730","https://openalex.org/W2971133799","https://openalex.org/W4319791967","https://openalex.org/W4226326207","https://openalex.org/W3005010504","https://openalex.org/W7080119995","https://openalex.org/W2804609220","https://openalex.org/W2887850883"],"abstract_inverted_index":{"To":[0],"perform":[1],"Bayesian":[2,58],"inference":[3],"for":[4,8,119,129],"stochastic":[5],"simulator":[6],"models":[7,166],"which":[9],"the":[10,22,55,69,72,80,84,127,145,149,160],"likelihood":[11,95,113],"is":[12],"not":[13],"accessible,":[14],"Likelihood-Free":[15],"Inference":[16],"(LFI)":[17],"relies":[18],"on":[19,83],"simulations":[20,34,64],"from":[21,54,79,126,179],"model.":[23],"Standard":[24],"LFI":[25],"methods":[26],"can":[27,116,152],"be":[28,153],"split":[29],"according":[30,48],"to":[31,37,44,49,68,89,97,123,141,175],"how":[32],"these":[33],"are":[35,65,106],"used:":[36],"build":[38],"an":[39],"explicit":[40],"Surrogate":[41],"Likelihood,":[42],"or":[43],"accept/reject":[45],"parameter":[46],"values":[47],"a":[50,91,176],"measure":[51],"of":[52,71,132,148],"distance":[53],"observations":[56,133],"(Approximate":[57],"Computation":[59],"(ABC)).":[60],"In":[61],"both":[62],"cases,":[63],"adaptively":[66],"tailored":[67],"value":[70],"observation.":[73],"Here,":[74],"we":[75,100,115,171],"generate":[76],"parameter-simulation":[77],"pairs":[78],"model":[81,136,178],"independently":[82],"observation,":[85],"and":[86],"use":[87,101],"them":[88],"learn":[90],"conditional":[92],"exponential":[93,150],"family":[94,151],"approximation;":[96],"parametrize":[98],"it,":[99],"Neural":[102],"Networks":[103],"whose":[104],"weights":[105],"tuned":[107],"with":[108,138,167],"Score":[109],"Matching.":[110],"With":[111],"our":[112,173],"approximation,":[114],"employ":[117],"MCMC":[118],"doubly":[120],"intractable":[121],"distributions":[122],"draw":[124],"samples":[125],"posterior":[128],"any":[130],"number":[131],"without":[134],"additional":[135],"simulations,":[137],"performance":[139],"competitive":[140],"comparable":[142],"approaches.":[143],"Further,":[144],"sufficient":[146],"statistics":[147],"used":[154],"as":[155],"summaries":[156],"in":[157,163],"ABC,":[158],"outperforming":[159],"state-of-the-art":[161],"method":[162,174],"five":[164],"different":[165],"known":[168],"likelihood.":[169],"Finally,":[170],"apply":[172],"challenging":[177],"meteorology.":[180]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-01-05T00:00:00"}