A great variability induces a multi-manifold parameter space which disturbs the AAM convergence.
We define ρ as the number of photons per unit area of the 2-manifold parameter space of the emitter which we measure a priori to feature detection.
We find that the principal eigenvalue switches sign exactly at the codimension one manifold in parameter space that separates the domain of existence of a nontrivial solution from the domain of nonexistence.
IG is the study of manifolds in the parameter space of probability distributions, using the tools of differential geometry [5].
Thus, we can find manifolds in the parameter space that result in the same quantities of interest using different reaction mechanisms.
The transitivity-induced parameter dependence makes the pertinent parameter space a complex manifold.
In the unfolding parameter space there are manifolds (e.g. curves, surfaces) of lower codimension bifurcation points.
Within an operating region in the control input space, the minimal distance in the system parameter space to bifurcation manifolds provides a quantitative measure of the robustness of the system to environmental perturbations.
For an ODE system, let Σ denote a bifurcation manifold of interest, consisting of sets in parameter space P for which structural stability breaks down [ 1].
As a matter of fact, since the feasible manifold is of lower dimension than the entire parameter space, methods that do not consider the underlying thermodynamic and non-thermodynamic constraints will spend most time searching the immense infeasible portions of the parameter space.
Fortunately, there is a common case where selection of the prior is almost universally accepted, and that is when the model parameters form an Abelian Lie group [ 9], or in other words, when the parameter space is a commutative group on a differentiable manifold.
