Fig. 1
Canonical correlations and identifiability. a Illustrative view of the sensitivity vectors S i . b Conceptual illustration of the canonical correlations. Two subsets of sensitivity vectors represented as linear subspaces (planes Ω A and Ω B ). Canonical vectors on the planes are found to yield maximum cosine. In a two-dimensional subspace case, the second canonical vectors u 2,v 2 are required to be perpendicular to the first ones. c The introduced δ-condition requires that each parameter θ i is correlated less than δ with the remaining parameters θ −i =(θ 1,..,θ i−1,θ i+1,…,θ l ). It can be interpreted in terms of how variance of the estimates changes when a single parameter and all model parameters are estimated. Parameter θ 0 denotes the linear combination of θ −i maximally correlated with θ i , i.e. θ 0=lin∗{θ −i }. d Mutual information as a measure of similarity between two parameter sets θ A ,θ B , which span linear subspaces Ω A ,Ω B interpreted in terms of the asymptotic posterior \(P(\hat {\theta }|\theta)\)