{"id":"https://openalex.org/W4281250079","doi":"https://doi.org/10.48550/arxiv.2205.09680","title":"Metrics of calibration for probabilistic predictions","display_name":"Metrics of calibration for probabilistic predictions","publication_year":2022,"publication_date":"2022-05-19","ids":{"openalex":"https://openalex.org/W4281250079","doi":"https://doi.org/10.48550/arxiv.2205.09680"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2205.09680","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.09680","pdf_url":"https://arxiv.org/pdf/2205.09680","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,"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":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.09680","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051152384","display_name":"Imanol Arrieta-Ibarra","orcid":"https://orcid.org/0000-0003-4402-2618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arrieta-Ibarra, Imanol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011296970","display_name":"Paman Gujral","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gujral, Paman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044033992","display_name":"Jonathan Tannen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tannen, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063476648","display_name":"Mark Tygert","orcid":"https://orcid.org/0000-0002-6389-799X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tygert, Mark","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004723199","display_name":"Cherie Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Cherie","raw_affiliation_strings":[],"raw_orcid":null,"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":true,"cited_by_count":9,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.972100019454956,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6532357931137085},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6075338125228882},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5976911187171936},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5553243160247803},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.5021238327026367},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.46074214577674866},{"id":"https://openalex.org/keywords/kurtosis","display_name":"Kurtosis","score":0.4583349823951721},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.44586479663848877},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.4308277368545532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4226796329021454},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.41992902755737305},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4172179102897644},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3706499934196472},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31004711985588074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15281546115875244},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.13890790939331055}],"concepts":[{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6532357931137085},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6075338125228882},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5976911187171936},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5553243160247803},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.5021238327026367},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.46074214577674866},{"id":"https://openalex.org/C166963901","wikidata":"https://www.wikidata.org/wiki/Q287251","display_name":"Kurtosis","level":2,"score":0.4583349823951721},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.44586479663848877},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.4308277368545532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4226796329021454},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.41992902755737305},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4172179102897644},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3706499934196472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31004711985588074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15281546115875244},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.13890790939331055},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2205.09680","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.09680","pdf_url":"https://arxiv.org/pdf/2205.09680","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,"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2205.09680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.09680","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,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.09680","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.09680","pdf_url":"https://arxiv.org/pdf/2205.09680","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,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4225568567","https://openalex.org/W4286378979","https://openalex.org/W1496883226","https://openalex.org/W4297337052","https://openalex.org/W2028605949","https://openalex.org/W2282665605","https://openalex.org/W1528102763","https://openalex.org/W2585385340","https://openalex.org/W1569550976","https://openalex.org/W4297926828"],"abstract_inverted_index":{"Predictions":[0],"are":[1,80,127],"often":[2],"probabilities;":[3],"e.g.,":[4],"a":[5,15,148,152,180,235],"prediction":[6],"could":[7],"be":[8],"for":[9,107,164,228],"precipitation":[10],"tomorrow,":[11],"but":[12],"with":[13,23,64,115],"only":[14],"30%":[16],"chance.":[17],"Given":[18],"such":[19],"probabilistic":[20],"predictions":[21,42],"together":[22],"the":[24,41,44,51,57,60,84,88,102,108,120,124,165,168,229,238,245,260],"actual":[25],"outcomes,":[26],"\"reliability":[27],"diagrams\"":[28],"help":[29],"detect":[30],"and":[31,43,53,90,209],"diagnose":[32],"statistically":[33],"significant":[34],"discrepancies":[35],"--":[36,39],"so-called":[37],"\"miscalibration\"":[38],"between":[40,87],"outcomes.":[45],"The":[46,140,156,189],"canonical":[47],"reliability":[48,149],"diagrams":[49],"histogram":[50,62],"observed":[52,89],"expected":[54,91],"values":[55,92],"of":[56,76,83,104,123,144,167,170,237],"predictions;":[58],"replacing":[59],"hard":[61],"binning":[63,218],"soft":[65],"kernel":[66,137,220],"density":[67,138,221],"estimation":[68,222],"is":[69,111,130,192],"another":[70],"common":[71],"practice.":[72],"But,":[73],"which":[74,184],"widths":[75],"bins":[77,246,261],"or":[78,135,219,241,247,262],"kernels":[79,248,263],"best?":[81],"Plots":[82],"cumulative":[85,157,171,190],"differences":[86,172],"largely":[93],"avoid":[94],"this":[95],"question,":[96],"by":[97,206],"displaying":[98],"miscalibration":[99,145],"directly":[100],"as":[101,151,234,271],"slopes":[103],"secant":[105,125],"lines":[106,126],"graphs.":[109],"Slope":[110],"easy":[112],"to":[113,133,161,179,231],"perceive":[114],"quantitative":[116],"precision,":[117],"even":[118],"when":[119],"constant":[121],"offsets":[122],"irrelevant;":[128],"there":[129],"no":[131],"need":[132],"bin":[134],"perform":[136],"estimation.":[139],"existing":[141],"standard":[142],"metrics":[143,163,215],"each":[146],"summarize":[147],"diagram":[150],"single":[153],"scalar":[154,162],"statistic.":[155],"plots":[158],"naturally":[159],"lead":[160],"deviation":[166],"graph":[169,183],"away":[173,250,270],"from":[174,187],"zero;":[175],"good":[176],"calibration":[177],"corresponds":[178],"horizontal,":[181],"flat":[182],"deviates":[185],"little":[186],"zero.":[188],"approach":[191],"currently":[193],"unconventional,":[194],"yet":[195],"offers":[196],"many":[197],"favorable":[198],"statistical":[199,226],"properties,":[200],"guaranteed":[201],"via":[202],"mathematical":[203],"theory":[204],"backed":[205],"rigorous":[207],"proofs":[208],"illustrative":[210],"numerical":[211],"examples.":[212],"In":[213],"particular,":[214],"based":[216],"on":[217],"unavoidably":[223],"must":[224],"trade-off":[225],"confidence":[227],"ability":[230],"resolve":[232],"variations":[233],"function":[236],"predicted":[239],"probability":[240],"vice":[242],"versa.":[243],"Widening":[244],"averages":[249],"random":[251],"noise":[252],"while":[253,267],"giving":[254],"up":[255],"some":[256],"resolving":[257,265],"power.":[258],"Narrowing":[259],"enhances":[264],"power":[266],"not":[268],"averaging":[269],"much":[272],"noise.":[273]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}