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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Bibliographic Details
Published in:Automatica (Oxford) Vol. 45; no. 1; pp. 173 - 179
Main Authors: Pillonetto, Gianluigi, De Nicolao, Giuseppe, Chierici, Marco, Cobelli, Claudio
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 2009
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2008.06.003