Results 11 to 20 of about 10,339,939 (316)
Being Uncertain in Chromatographic Calibration—Some Unobvious Details in Experimental Design
Molecules, 2021 This is an introductory tutorial and review about the uncertainty problem in chromatographic calibration. It emphasizes some unobvious, but important details influencing errors in the calibration curve estimation, uncertainty in prediction, as well as ...
Łukasz Komsta +2 more
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Optimal Geometry Analysis for Target Localization With Bayesian Priors
IEEE Access, 2021 Relative sensor-target geometry is well known to significantly affect the performance of target localization using a sensor network. This article analyzes the optimal sensor-target geometries for the problem of target localization with Bayesian priors ...
Ngoc Hung Nguyen
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Modified Bayesian D-Optimality for Accelerated Degradation Test Design With Model Uncertainty
IEEE Access, 2019 Accelerated degradation test (ADT) has become the main method to assess system reliability. In ADT, Bayesian D-optimality criterion is an effective objective function to deal with the model parameter uncertainty in designing an ADT plan.
Yong Yu +3 more
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Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality [PDF]
, 2004 An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression
Harris, C. J. +5 more
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Optimal Designs for Generalized Pareto Model [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2019 The generalized Pareto model plays an important role in modelling extreme events. Hosking and Wallis (1987) discussed the parameter and quantile estimation for generalized Pareto distribution.
Poonam Singh, Ashok Kumar
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Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality [PDF]
, 2004 A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules.
Harris, C. J. +3 more
core +1 more source
Journal of Geospatial Information Science and Engineering, 2021 Kegiatan rehabilitasi dan pemeliharaan di Jembatan Talang Bowong membutuhkan pemantauan gerakan massa tanah secara real-time dan kontinu. Penelitian ini berusaha mendesain jaring kontrol pemantauan gerakan massa tanah secara geometrik dengan data ...
Ghea Ayunda Siami +2 more
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R/D optimal linear prediction [PDF]
IEEE Transactions on Speech and Audio Processing, 2000 A common technique to extend linear prediction to nonstationary signals is time segmentation: the signal is split into small portions and the modelization is carried out locally. The accuracy of the analysis is, however, dependent on the window size and on the signal characteristics, so that the problem of finding a good segmentation is crucial to the ...
Paolo Prandoni, Martin Vetterli
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, 2021 Optimality of syntactic dependency distances Preprocessed data and code for the article Optimality of Syntactic Dependency Distances. This is the first release of the Optimality of Syntactic Dependency Distances repository at Github.com. The sole purpose
Carlos Gómez-Rodríguez +2 more
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A Review on Optimal Subsampling Method [PDF]
BIO Web of ConferencesOptimal subsampling method is an efficient method for massive data because it can not only downsize the data amount but also save computational time. Subsampling methods have been essential to statistical analysis throughout history.
Xu Yuan
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