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We constructed two Markov models to estimate the extent of overdiagnosis: one to estimate screening parameters based on the Age trial data (parameter estimation model), and one to estimate overdiagnosis based on parameter estimates from the first model (overdiagnosis model).
The method, called the Characteristic Operating Pattern (CHOP) method, is a visually-based aggregation method that clusters reference data based on parameter values rather than time of occurrence, thereby preserving important information on short-term relations between the relevant operating parameters.
We also propose some new algorithms based on parameter optimization and evaluate their performance together with the existing ones.
An individual-based model then determines the detection probabilities of the food patches by bees, based on parameter values gathered from the flight patterns of radar-tracked honeybees and bumblebees.
Practical calculating method for k was proposed based on parameter analysis results.
It is based on parameter algebraic matrix equations and LMI conditions.
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Our approach is based on parameter-dependent Lyapunov functions.
Then a method based on parameter-induced stochastic resonance (PSR) is presented.
The proposed methods are based on parameter-dependent Lyapunov functions and are given in terms of linear matrix inequalities.
Moreover, since the synthesis condition is based on parameter-dependent Lyapunov functions, it gives less conservative results than existing methods.
The results are based on parameter-dependent Lyapunov functions obtained from the feasibility of a set of linear matrix inequalities defined at the vertices of polytopic systems.
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