{"id":"https://openalex.org/W7162508411","doi":"https://doi.org/10.48550/arxiv.2605.27093","title":"Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix","display_name":"Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162508411","doi":"https://doi.org/10.48550/arxiv.2605.27093"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27093","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093494561","display_name":"Kane Warrior","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Warrior, Kane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062155998","display_name":"Dalia Chakrabarty","orcid":"https://orcid.org/0000-0003-1246-4235"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakrabarty, Dalia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.978600025177002,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.978600025177002,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.003700000001117587,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.002899999963119626,"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/wishart-distribution","display_name":"Wishart distribution","score":0.7912999987602234},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.640500009059906},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5856000185012817},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5827000141143799},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5813999772071838},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5419999957084656},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5299000144004822},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.5199000239372253},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5115000009536743},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4697999954223633}],"concepts":[{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.7912999987602234},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5856000185012817},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5827000141143799},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5813999772071838},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5436999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5419999957084656},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.5199000239372253},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5115000009536743},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4697999954223633},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45179998874664307},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.4429999887943268},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4383000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.397599995136261},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.396699994802475},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C40851411","wikidata":"https://www.wikidata.org/wiki/Q3258368","display_name":"Inverse-Wishart distribution","level":4,"score":0.37290000915527344},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.3167000114917755},{"id":"https://openalex.org/C118006245","wikidata":"https://www.wikidata.org/wiki/Q6792079","display_name":"Mat\u00e9rn covariance function","level":5,"score":0.31630000472068787},{"id":"https://openalex.org/C176917957","wikidata":"https://www.wikidata.org/wiki/Q7430596","display_name":"Scatter matrix","level":4,"score":0.310699999332428},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.29660001397132874},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C72010251","wikidata":"https://www.wikidata.org/wiki/Q5265688","display_name":"Determinantal point process","level":4,"score":0.26840001344680786},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.26350000500679016},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C205555498","wikidata":"https://www.wikidata.org/wiki/Q505588","display_name":"CMA-ES","level":4,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27093","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27093","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":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":{"In":[0],"probabilstic":[1],"supervised":[2],"learning":[3,50,134],"of":[4,13,26,34,42],"an":[5],"input-output":[6],"relationship":[7],"-":[8,18,146],"as":[9],"a":[10,14,72,93,102,154],"sample":[11],"function":[12,33,56],"Gaussian":[15],"Process":[16],"(GP)":[17],"priors":[19],"are":[20],"typically":[21],"specified":[22],"for":[23,76,126],"the":[24,27,31,35,38,43,49,54,77,85,111,120,132],"hyperparameters":[25,87],"kernel":[28,86],"that":[29,115],"parametrises":[30],"covariance":[32,40,78,121],"GP,":[36],"where":[37],"induced":[39],"matrix":[41,122],"(resulting":[44],"multivariate":[45],"Normal)":[46],"likelihood,":[47],"governs":[48],"and":[51,151],"prediction.":[52],"When":[53],"sought":[55],"is":[57],"highly":[58],"multivariate,":[59],"multiple":[60],"lengthscale":[61],"parameters":[62],"must":[63],"be":[64,124],"learnt":[65],"simultaneously,":[66],"making":[67],"inference":[68,83],"difficult.":[69],"We":[70,136],"develop":[71],"``self-assembled''":[73],"Wishart":[74],"prior":[75,117,139],"matrix,":[79,106],"while":[80],"undertaking":[81],"Bayesian":[82],"on":[84,119,148,153],"using":[88],"MCMC.":[89],"The":[90],"construction":[91],"uses":[92],"look-back":[94],"window":[95],"over":[96],"recent":[97],"MCMC":[98],"iterations":[99],"to":[100,110],"define":[101],"time-step":[103],"dependent":[104],"scale":[105],"thereby":[107],"introducing":[108],"adaptiveness":[109],"chain.":[112],"Results":[113],"suggest":[114],"direct":[116],"specification":[118],"can":[123],"useful":[125],"diagnosing":[127],"weakly":[128],"informative":[129],"inputs":[130],"within":[131],"GP-based":[133],"paradigm.":[135],"support":[137],"our":[138],"development":[140],"with":[141],"two":[142],"distinct":[143],"empirical":[144],"illustrations":[145],"one":[147],"synthetic":[149],"data,":[150],"another":[152],"real-world":[155],"dataset.":[156]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-28T00:00:00"}
