Fast algorithms for nonparametric population modeling of large data sets

Population models are widely applied in biomedical data analysis since they characterize both the average and individual responses of a population of subjects. In the absence of a reliable mechanistic model, one can resort to the Bayesian nonparametric approach that models the individual curves as G...

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Vydáno v:	Automatica (Oxford) Ročník 45; číslo 1; s. 173 - 179
Hlavní autoři:	Pillonetto, Gianluigi, De Nicolao, Giuseppe, Chierici, Marco, Cobelli, Claudio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 2009 Elsevier
Témata:	Applied sciences Bayesian estimation Computer science; control theory; systems Control theory. Systems Estimation theory Exact sciences and technology Gaussian processes Glucose metabolism Nonparametric identification Glucose metabolism Bayesian estimation Nonparametric identification Gaussian processes Estimation theory Bayes estimation Data analysis Non parametric estimation Data processing Client server architecture Metabolism Modeling Gaussian process System identification Fast algorithm Sampling Biomedical engineering
ISSN:	0005-1098, 1873-2836
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Abstract	Population models are widely applied in biomedical data analysis since they characterize both the average and individual responses of a population of subjects. In the absence of a reliable mechanistic model, one can resort to the Bayesian nonparametric approach that models the individual curves as Gaussian processes. This paper develops an efficient computational scheme for estimating the average and individual curves from large data sets collected in standardized experiments, i.e. with a fixed sampling schedule. It is shown that the overall scheme exhibits a “client–server” architecture. The server is in charge of handling and processing the collective data base of past experiments. The clients ask the server for the information needed to reconstruct the individual curve in a single new experiment. This architecture allows the clients to take advantage of the overall data set without violating possible privacy and confidentiality constraints and with negligible computational effort.
AbstractList	Population models are widely applied in biomedical data analysis since they characterize both the average and individual responses of a population of subjects. In the absence of a reliable mechanistic model, one can resort to the Bayesian nonparametric approach that models the individual curves as Gaussian processes. This paper develops an efficient computational scheme for estimating the average and individual curves from large data sets collected in standardized experiments, i.e. with a fixed sampling schedule. It is shown that the overall scheme exhibits a “client–server” architecture. The server is in charge of handling and processing the collective data base of past experiments. The clients ask the server for the information needed to reconstruct the individual curve in a single new experiment. This architecture allows the clients to take advantage of the overall data set without violating possible privacy and confidentiality constraints and with negligible computational effort.
Author	Chierici, Marco De Nicolao, Giuseppe Cobelli, Claudio Pillonetto, Gianluigi
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Cites_doi	10.3109/03602538409015063 10.1109/TBME.2007.902240 10.1016/j.automatica.2006.12.024 10.1023/A:1020206907668 10.1007/BF02353463 10.1152/ajpendo.2001.280.1.E179 10.2337/db05-1692 10.1023/A:1025769431364 10.1109/TMI.2004.824243 10.1023/A:1022920403166
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Keywords	Glucose metabolism Bayesian estimation Nonparametric identification Gaussian processes Estimation theory Bayes estimation Data analysis Non parametric estimation Data processing Client server architecture Metabolism Modeling Gaussian process System identification Fast algorithm Sampling Biomedical engineering
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SubjectTerms	Applied sciences Bayesian estimation Computer science; control theory; systems Control theory. Systems Estimation theory Exact sciences and technology Gaussian processes Glucose metabolism Nonparametric identification
Title	Fast algorithms for nonparametric population modeling of large data sets
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