Parameter redundancy

2014 ◽  
pp. 209-228
Keyword(s):  
Parameter Redundancy

Parameter redundancy with covariates

Biometrika ◽  
2010 ◽  
Vol 97 (4) ◽  
pp. 1002-1005 ◽  
Author(s):  
Diana J. Cole ◽  
Byron J. T. Morgan
Keyword(s):  
Parameter Redundancy

Parameter Identifiability in Statistical Machine Learning: A Review

2017 ◽  
Vol 29 (5) ◽  
pp. 1151-1203 ◽  
Author(s):  
Zhi-Yong Ran ◽  
Bao-Gang Hu
Keyword(s):  
Machine Learning ◽  
Optimization Theory ◽  
Statistical Machine Learning ◽  
Parameter Identifiability ◽  
Advantages And Disadvantages ◽  
Riemann Geometry ◽  
Parameter Redundancy ◽  
Art Research ◽  
New Perspective ◽  
Review Criteria

This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.

Parameter redundancy in mark-recovery models

2012 ◽  
Vol 54 (4) ◽  
pp. 507-523 ◽  
Author(s):  
Diana J. Cole ◽  
Byron J. T. Morgan ◽  
Edward A. Catchpole ◽  
Ben A. Hubbard
Keyword(s):  
Parameter Redundancy ◽  
Recovery Models

Kinetic analysis of cell spreading. I. Theory and modelling of curves

1983 ◽  
Vol 61 (1) ◽  
pp. 365-373
Author(s):  
W.G. Bardsley ◽  
J.D. Aplin
Keyword(s):  
Experimental Data ◽  
Mathematical Models ◽  
Kinetic Analysis ◽  
Goodness Of Fit ◽  
Cell Spreading ◽  
Parameter Redundancy

The spreading of cells on suitably treated surfaces is briefly discussed and the need to analyse this phenomenon numerically is emphasized. Possible mathematical models for fitting experimental data are classified as statistical, kinetic or empirical and examples of each of these types are given. A possible protocol for analysing cell spreading kinetics and determining goodness of fit and parameter redundancy is presented.

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