{"id":"https://openalex.org/W7171669648","doi":"https://doi.org/10.48550/arxiv.2607.25376","title":"Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance","display_name":"Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance","publication_year":2026,"publication_date":"2026-07-28","ids":{"openalex":"https://openalex.org/W7171669648","doi":"https://doi.org/10.48550/arxiv.2607.25376"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.25376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.25376","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.25376","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034821785","display_name":"Pei-Hsuan Hsia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsia, Pei-Hsuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133085271","display_name":"Lars H. Heyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heyen, Lars H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010580917","display_name":"Arvid Weyrauch","orcid":"https://orcid.org/0000-0002-2684-0927"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weyrauch, Arvid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143901232","display_name":"Markus Goetz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goetz, Markus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030034618","display_name":"Achim Streit","orcid":"https://orcid.org/0000-0002-5065-469X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Streit, Achim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000019904","display_name":"Sebastian Krumscheid","orcid":"https://orcid.org/0000-0002-1421-5403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krumscheid, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132737514","display_name":"Charlotte Debus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Debus, Charlotte","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":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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.4514999985694885,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.4514999985694885,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.2304999977350235,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.13590000569820404,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.6384999752044678},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5026000142097473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48539999127388},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4726000130176544},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.4659000039100647},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45179998874664307},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43140000104904175},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.39320001006126404},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.39169999957084656}],"concepts":[{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.6384999752044678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5562000274658203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48539999127388},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.4659000039100647},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45179998874664307},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43140000104904175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40149998664855957},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.39320001006126404},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.39169999957084656},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.38609999418258667},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.3483000099658966},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3328999876976013},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.325300008058548},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2985999882221222},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C171383496","wikidata":"https://www.wikidata.org/wiki/Q2497477","display_name":"Generalized normal distribution","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.2612000107765198},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C83247935","wikidata":"https://www.wikidata.org/wiki/Q7234227","display_name":"Posterior predictive distribution","level":5,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.25376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.25376","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.25376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.25376","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":[0,113,189],"Bayesian":[1],"neural":[2],"networks":[3],"(BNNs),":[4],"variational":[5],"inference":[6],"is":[7,59,118],"a":[8,16,54,69,174],"widely":[9],"adopted":[10],"framework":[11],"for":[12,64,72,97,128],"modeling":[13],"uncertainty":[14],"in":[15],"distributional":[17],"way,":[18],"with":[19],"the":[20,27,35,40,87,98,102,108,129,134,180],"evidence":[21],"lower":[22],"bound":[23],"(ELBO)":[24],"serving":[25],"as":[26,39,145],"standard":[28,159],"objective":[29],"function.":[30],"Several":[31],"distributions":[32,49,93],"contribute":[33],"to":[34,61,119,197,205],"ELBO":[36],"loss,":[37],"such":[38,144],"prior,":[41],"approximated":[42,52],"posterior,":[43],"and":[44,67,155,183,187],"likelihood":[45,109,130,141,176],"distribution.":[46],"Typically,":[47],"these":[48],"are":[50],"all":[51],"by":[53,123],"Gaussian":[55,175],"distribution,":[56,177],"since":[57],"it":[58],"easy":[60,204],"compute,":[62],"allows":[63],"reparameterized":[65],"gradients,":[66],"provides":[68],"closed-form":[70],"loss":[71],"training.":[73],"However,":[74],"several":[75,140],"works":[76],"have":[77,94],"highlighted":[78],"that":[79,166],"this":[80,114,121],"assumption":[81],"may":[82],"not":[83],"generally":[84],"hold,":[85],"posing":[86],"risk":[88],"of":[89,104,179],"model":[90],"misspecification.":[91],"Alternative":[92],"been":[95],"proposed":[96],"prior":[99],"specifically,":[100],"while":[101,201],"effect":[103],"distribution":[105,110,131,142,182],"choice":[106],"on":[107,152],"remains":[111],"unexplored.":[112],"work,":[115],"our":[116],"aim":[117],"close":[120],"gap":[122],"investigating":[124],"whether":[125],"alternative":[126],"assumptions":[127],"can":[132,194],"outperform":[133],"commonly":[135],"used":[136],"Gaussian.":[137],"We":[138],"compare":[139],"assumptions,":[143],"skewed":[146],"or":[147],"heavy-tailed,":[148],"across":[149],"regression":[150],"tasks":[151],"both":[153],"artificial":[154],"real-world":[156],"datasets":[157],"using":[158],"multilayer":[160],"perceptrons":[161],"(MLPs).":[162],"Our":[163],"findings":[164],"demonstrate":[165],"Student's":[167,192],"t":[168,193],"yields":[169],"better":[170],"predictive":[171],"performance":[172],"than":[173],"independent":[178],"data":[181],"MLP":[184],"architecture":[185],"(depth":[186],"width).":[188],"some":[190],"cases,":[191],"also":[195],"lead":[196],"shorter":[198],"training":[199],"times,":[200],"still":[202],"being":[203],"implement.":[206]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2026-07-30T00:00:00"}