A Computational Study of Reduction Techniques for the Minimum Connectivity Inference Problem

2019 ◽  
pp. 135-148 ◽  
Author(s):  
Muhammad Abid Dar ◽  
Andreas Fischer ◽  
John Martinovic ◽  
Guntram Scheithauer
Keyword(s):  
Computational Study ◽  
Inference Problem ◽  
Reduction Techniques ◽  
Connectivity Inference

An improved flow-based formulation and reduction principles for the minimum connectivity inference problem

Optimization ◽  
2018 ◽  
Vol 68 (10) ◽  
pp. 1963-1983 ◽  
Author(s):  
Muhammad Abid Dar ◽  
Andreas Fischer ◽  
John Martinovic ◽  
Guntram Scheithauer
Keyword(s):  
Inference Problem ◽  
Connectivity Inference

scholarly journals Kernel Methods for Nonlinear Connectivity Detection

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 610
Author(s):  
Lucas Massaroppe ◽  
Luiz Baccalá
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Feature Space ◽  
Original Data ◽  
Model Diagnostics ◽  
Inference Problem ◽  
Short Time Series ◽  
Data Space ◽  
Short Time ◽  
Connectivity Inference

In this paper, we show that the presence of nonlinear coupling between time series may be detected using kernel feature space F representations while dispensing with the need to go back to solve the pre-image problem to gauge model adequacy. This is done by showing that the kernelized auto/cross sequences in F can be computed from the model rather than from prediction residuals in the original data space X . Furthermore, this allows for reducing the connectivity inference problem to that of fitting a consistent linear model in F that works even in the case of nonlinear interactions in the X -space which ordinary linear models may fail to capture. We further illustrate the fact that the resulting F -space parameter asymptotics provide reliable means of space model diagnostics in this space, and provide straightforward Granger connectivity inference tools even for relatively short time series records as opposed to other kernel based methods available in the literature.

Integer linear programming formulations for the minimum connectivity inference problem and model reduction principles

2021 ◽  
Vol 40 ◽  
pp. 100623
Author(s):  
Muhammad Abid Dar ◽  
Andreas Fischer ◽  
John Martinovic ◽  
Guntram Scheithauer
Keyword(s):  
Linear Programming ◽  
Model Reduction ◽  
Integer Linear Programming ◽  
Inference Problem ◽  
Connectivity Inference

Catalogue reduction techniques

1978 ◽  
Vol 48 ◽  
pp. 389-390 ◽  
Author(s):  
Chr. de Vegt
Keyword(s):  
Adjustment Process ◽  
Proper Motions ◽  
Measuring Process ◽  
Aerial Photogrammetry ◽  
Reduction Techniques ◽  
Observational Technique ◽  
The Common ◽  
Astrometric Data

AbstractReduction techniques as applied to astrometric data material tend to split up traditionally into at least two different classes according to the observational technique used, namely transit circle observations and photographic observations. Although it is not realized fully in practice at present, the application of a blockadjustment technique for all kind of catalogue reductions is suggested. The term blockadjustment shall denote in this context the common adjustment of the principal unknowns which are the positions, proper motions and certain reduction parameters modelling the systematic properties of the observational process. Especially for old epoch catalogue data we frequently meet the situation that no independent detailed information on the telescope properties and other instrumental parameters, describing for example the measuring process, is available from special calibration observations or measurements; therefore the adjustment process should be highly self-calibrating, that means: all necessary information has to be extracted from the catalogue data themselves. Successful applications of this concept have been made already in the field of aerial photogrammetry.

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