{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:21:39Z","timestamp":1740169299439,"version":"3.37.3"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Inha University Research Grant and the National Research Foundation of Korea (NRF) Grant funded by the Korea Government","doi-asserted-by":"publisher","award":["2016M2B8A7952915"],"award-info":[{"award-number":["2016M2B8A7952915"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3237395","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T19:16:27Z","timestamp":1673896587000},"page":"7109-7116","source":"Crossref","is-referenced-by-count":0,"title":["Curve Fitting Algorithm of Functional Radiation-Response Data Using Bayesian Hierarchical Gaussian Process Regression Model"],"prefix":"10.1109","volume":"11","author":[{"given":"Kwang-Woo","family":"Jung","sequence":"first","affiliation":[{"name":"Radiation Research Division, Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7831-6353","authenticated-orcid":false,"given":"Jaeoh","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Data Science, Inha University, Incheon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ho-Jin","family":"Jung","sequence":"additional","affiliation":[{"name":"Insilicogen Inc, Yongin, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung-Won","family":"Seo","sequence":"additional","affiliation":[{"name":"Insilicogen Inc, Yongin, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Man","family":"Hong","sequence":"additional","affiliation":[{"name":"Insilicogen Inc, Yongin, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoung-Woo","family":"Bai","sequence":"additional","affiliation":[{"name":"Radiation Research Division, Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1757-9122","authenticated-orcid":false,"given":"Seongil","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Statistics, Inha University, Incheon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.6028\/jres.126.014"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0038-1640194"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1585\/pfr.16.1402089"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-7687.2007.00585.x"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511802478.006"},{"key":"ref6","first-page":"195","article-title":"One-shot learning with a hierarchical nonparametric Bayesian model","volume-title":"Proc. ICML Workshop Unsupervised Transf. Learn.","author":"Salakhutdinov"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3128271"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2858265"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1201\/b11038"},{"volume-title":"Probabilistic Machine Learning: An Introduction","year":"2022","author":"Murphy","key":"ref11"},{"volume-title":"Probability and Measure","year":"1995","author":"Billingsley","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1214\/15-BA967"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12705"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/68.1.265"},{"key":"ref16","first-page":"21","article-title":"Inferring parameters and structure of latent variable models by variational Bayes","volume-title":"Proc. 15th Conf. Uncertainty Artif. Intell.","author":"Attias"},{"issue":"2","key":"ref17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1023\/A:1007665907178","article-title":"An introduction to variational methods for graphical models","volume":"37","author":"Jordan","year":"1999","journal-title":"Mach. Learn."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1561\/2200000001"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref20","first-page":"1","article-title":"Automatic variational inference in Stan","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kucukelbir"},{"issue":"14","key":"ref21","first-page":"1","article-title":"Automatic differentiation variational inference","volume":"18","author":"Kucukelbir","year":"2017","journal-title":"J. Mach. Learn. Res."},{"volume-title":"Handbook of Mathematical Functions","year":"1965","author":"Abramowitz","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref24","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref25","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rezende"},{"key":"ref26","first-page":"1971","article-title":"Doubly stochastic variational Bayes for non-conjugate inference","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Titsias"},{"volume-title":"Stan Modeling Language Users Guide and Reference Manual","year":"2022","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2638586"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177011136"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1214\/20-BA1221"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-017-9729-7"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/b98888"},{"key":"ref34","first-page":"1","article-title":"Covariances, robustness, and variational Bayes","volume":"19","author":"Giordano","year":"2018","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10018221.pdf?arnumber=10018221","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T07:22:10Z","timestamp":1707808930000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10018221\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3237395","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}