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95% confidence regions are provided.
The second idea is based on the likelihood confidence regions.
The errors show the 95% confidence regions (±2σ).
Item parameter estimates are surrounded by 95% confidence regions.
The errors are in the 95% confidence regions.
Dynamical inferences need to be qualified by the risk of bifurcation boundaries crossing the confidence regions.
Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates.
Frequentist confidence regions (CRs) for the solution of the inversion problem are built.
Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used.
Application examples are shown by considering the construction of confidence regions and mixing models.
The parameter uncertainty is described by confidence regions in parameters space.
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