Bayesian calibration for multiple source regression model

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dc.contributor.authorIgnatyev, Dmitry I.
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2018-09-07T10:59:14Z
dc.date.available2018-09-07T10:59:14Z
dc.date.issued2018-08-20
dc.description.abstractIn large variety of practical applications, using information from different sources or different kind of data is a reasonable demand. The problem of studying multiple source data can be represented as a multi-task learning problem, and then the information from one source can help to study the information from the other source by extracting a shared common structure. From the other hand, parameter evaluations obtained from various sources can be confused and conflicting. This paper proposes a Bayesian based approach to calibrate data obtained from different sources and to solve nonlinear regression problem in the presence of heteroscedastisity of the multiple-source model. An efficient algorithm is developed for implementation. Using analytical and simulation studies, it is shown that the proposed Bayesian calibration improves the convergence rate of the algorithm and precision of the model. The theoretical results are supported by a synthetic example, and a real-world problem, namely, modeling unsteady pitching moment coefficient of aircraft, for which a recurrent neural network is constructed.en_UK
dc.identifier.citationDmitry I. Ignatyev, Hyo-Sang Shin and Antonios Tsourdos. Bayesian calibration for multiple source regression model. Neurocomputing, Volume 318, Issue November, 2018, pp. 55-64en_UK
dc.identifier.issn0925-2312
dc.identifier.urihttps://doi.org/10.1016/j.neucom.2018.08.027
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13458
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMultiple source dataen_UK
dc.subjectMultitask learningen_UK
dc.subjectHeteroscedastisityen_UK
dc.subjectBayesian calibrationen_UK
dc.subjectRegularizationen_UK
dc.subjectNonlinear regressionen_UK
dc.titleBayesian calibration for multiple source regression modelen_UK
dc.typeArticleen_UK

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