{"id":"https://openalex.org/W7129021442","doi":"https://doi.org/10.48550/arxiv.2602.12449","title":"Computationally sufficient statistics for Ising models","display_name":"Computationally sufficient statistics for Ising models","publication_year":2026,"publication_date":"2026-02-12","ids":{"openalex":"https://openalex.org/W7129021442","doi":"https://doi.org/10.48550/arxiv.2602.12449"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.12449","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126125968","display_name":"Abhijith Jayakumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jayakumar, Abhijith","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126139692","display_name":"Shreya Shukla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shukla, Shreya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063198169","display_name":"Marc Vuffray","orcid":"https://orcid.org/0000-0001-7999-9897"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vuffray, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084142327","display_name":"Andrey Y. Lokhov","orcid":"https://orcid.org/0000-0003-3269-7263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lokhov, Andrey Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126088636","display_name":"Sidhant Misra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Misra, Sidhant","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18642382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.5676000118255615,"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":0.5676000118255615,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.09950000047683716,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.048700001090765,"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/ising-model","display_name":"Ising model","score":0.7081000208854675},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.560699999332428},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5054000020027161},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.41620001196861267},{"id":"https://openalex.org/keywords/probably-approximately-correct-learning","display_name":"Probably approximately correct learning","score":0.40209999680519104},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.3788999915122986},{"id":"https://openalex.org/keywords/order-statistic","display_name":"Order statistic","score":0.3328000009059906}],"concepts":[{"id":"https://openalex.org/C51329190","wikidata":"https://www.wikidata.org/wiki/Q1076349","display_name":"Ising model","level":2,"score":0.7081000208854675},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.560699999332428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5299999713897705},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5054000020027161},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42239999771118164},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.40209999680519104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3982999920845032},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.3788999915122986},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.36640000343322754},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31189998984336853},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.2980000078678131},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26969999074935913},{"id":"https://openalex.org/C43596424","wikidata":"https://www.wikidata.org/wiki/Q98124957","display_name":"Probability and statistics","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C178197554","wikidata":"https://www.wikidata.org/wiki/Q1099110","display_name":"Sufficient statistic","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.12449","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.12449","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.12449","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.12449","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"Gibbs":[1,25],"distributions":[2,26],"using":[3],"only":[4,77],"sufficient":[5],"statistics":[6,125],"has":[7],"long":[8],"been":[9],"recognized":[10],"as":[11,101],"a":[12,49,78,102,117,152],"computationally":[13,20,66],"hard":[14],"problem.":[15],"On":[16],"the":[17,36,71,85,88,98,113,138,160,167],"other":[18],"hand,":[19],"efficient":[21,67],"algorithms":[22],"for":[23,65,116],"learning":[24,72,168],"rely":[27],"on":[28],"access":[29,75],"to":[30,52,63,76,111,127,136],"full":[31,50],"sample":[32,51],"configurations":[33],"generated":[34],"from":[35],"model.":[37],"For":[38],"many":[39],"systems":[40],"of":[41,81,90,130,159],"interest":[42],"that":[43,69,107,166],"arise":[44],"in":[45],"physical":[46],"contexts,":[47],"expecting":[48],"be":[53,171],"observed":[54],"is":[55,61,109,162],"not":[56],"practical,":[57],"and":[58,92,141,146,164],"hence":[59],"it":[60,108],"important":[62],"look":[64],"methods":[68],"solve":[70],"problem":[73,169],"with":[74,119,174],"limited":[79,177],"set":[80],"statistics.":[82],"We":[83,105,149],"examine":[84],"trade-offs":[86],"between":[87],"power":[89],"computation":[91],"observation":[93],"within":[94],"this":[95],"scenario,":[96],"employing":[97],"Ising":[99],"model":[100,114,118,161],"paradigmatic":[103],"example.":[104],"demonstrate":[106],"feasible":[110],"reconstruct":[112],"parameters":[115],"$\\ell_1$":[120],"width":[121],"$\u03b3$":[122],"by":[123],"observing":[124],"up":[126],"an":[128],"order":[129],"$O(\u03b3)$.":[131],"This":[132],"approach":[133],"allows":[134],"us":[135],"infer":[137],"model's":[139],"structure":[140,158],"also":[142,150],"learn":[143],"its":[144],"couplings":[145],"magnetic":[147],"fields.":[148],"discuss":[151],"setting":[153],"where":[154],"prior":[155],"information":[156],"about":[157],"available":[163],"show":[165],"can":[170],"solved":[172],"efficiently":[173],"even":[175],"more":[176],"observational":[178],"power.":[179]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-17T00:00:00"}
