{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:46:51Z","timestamp":1784562411753,"version":"3.55.0"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["#864686"],"award-info":[{"award-number":["#864686"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Contr."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1109\/tac.2021.3131988","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T20:30:40Z","timestamp":1638390640000},"page":"6460-6473","source":"Crossref","is-referenced-by-count":26,"title":["Prediction With Approximated Gaussian Process Dynamical Models"],"prefix":"10.1109","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-0338","authenticated-orcid":false,"given":"Thomas","family":"Beckers","sequence":"first","affiliation":[{"name":"Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7819-5926","authenticated-orcid":false,"given":"Sandra","family":"Hirche","sequence":"additional","affiliation":[{"name":"Chair of Information-oriented Control (ITR), Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1257","article-title":"Sparse Gaussian processes using pseudo-inputs","author":"snelson","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","first-page":"1939","article-title":"A unifying view of sparse approximate Gaussian process regression","volume":"6","author":"qui\u00f1onero-candela","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref33","first-page":"p","article-title":"Relationship between state-space and input-output models via observer Markov parameters","volume":"22","author":"phan","year":"1970","journal-title":"WIT Trans Built Environ"},{"key":"ref32","author":"keviczky","year":"1999","journal-title":"Nonlinear System Identification Input-Output Modeling Approach"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(95)00120-8"},{"key":"ref30","first-page":"1408","article-title":"Multi-kernel Gaussian processes","author":"melkumyan","year":"0","journal-title":"Proc 22nd Int Joint Conf Artif Intell"},{"key":"ref37","article-title":"Bayesian time series learning with Gaussian processes","author":"frigola-alcalde","year":"2016"},{"key":"ref36","first-page":"231","article-title":"On sparse variational methods and the Kullback–Leibler divergence between stochastic processes","volume":"51","author":"matthews","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref35","first-page":"1386","article-title":"A scenario-based optimal control approach for Gaussian process state space models","author":"umlauft","year":"0","journal-title":"Proc Eur Control Conf"},{"key":"ref34","first-page":"5309","article-title":"Identification of Gaussian process state space models","author":"eleftheriadis","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/13873950500068567"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2009.11.012"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1029\/2010JA016375"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20267-4_33"},{"key":"ref13","first-page":"71","article-title":"Adaptive home heating control through Gaussian process prediction and mathematical programming","author":"rogers","year":"0","journal-title":"Proc 2nd Int Workshop Agent Technol Energy Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2004.1383790"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2018.8550162"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21021-6"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1177\/0142331208095433"},{"key":"ref18","first-page":"1441","article-title":"Gaussian process dynamical models","author":"wang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2013.6580235"},{"key":"ref28","article-title":"An analytic comparison of regularization methods for Gaussian processes","author":"mohammadi","year":"2016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2012.04.005"},{"key":"ref27","volume":"4","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-011-0493-4"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2005.07.009"},{"key":"ref29","first-page":"1466","article-title":"It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals","author":"rakitsch","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2012.06.027"},{"key":"ref8","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2013.10.030"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"ref9","first-page":"3680","article-title":"Variational Gaussian process state-space models","author":"frigola","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","author":"nelles","year":"2013","journal-title":"Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ECC.2016.7810630"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2019.01.023"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2014.2387571"},{"key":"ref42","first-page":"502","article-title":"Risk-sensitive interaction control in uncertain manipulation tasks","author":"hern\u00e1ndez","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref24","first-page":"3156","article-title":"Bayesian inference and learning in Gaussian process state-space models with particle MCMC","author":"frigola","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127407018245"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7799247"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(98)00070-3"},{"key":"ref26","first-page":"5190","article-title":"Variational inference for Gaussian process models with linear complexity","author":"cheng","year":"0","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2006.05.011"},{"key":"ref25","first-page":"10292","article-title":"Efficiently sampling functions from Gaussian process posteriors","author":"wilson","year":"0","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Automatic Control"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9\/9969927\/09632353.pdf?arnumber=9632353","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:34:33Z","timestamp":1672083273000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9632353\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":44,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tac.2021.3131988","relation":{},"ISSN":["0018-9286","1558-2523","2334-3303"],"issn-type":[{"value":"0018-9286","type":"print"},{"value":"1558-2523","type":"electronic"},{"value":"2334-3303","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]}}}