Exact(1)
In addition, there are two covariance matrices: Φ the covariance matrix for ξ and Ψ the covariance matrix for ζ.
Similar(59)
Here C ξ ~ is the MSE matrix of ξ.
and V ^ k is an eigenvector matrix of Ξ ^ k ′ H k H k Ξ ^ k.
Then, we use (46) to obtain Σ ξ from Σ Ξ, where Σ ξ is the steady state covariance matrix of ξ t.
Similarly, we can get the channel state transition probability matrix of relay k for relay to destination channel as Ψ k =[ψ k (i,j ]L×L, and the channel state transition probability matrix for source to destination channel as Ξ k =[ξ k (i,j ]L×L.
Then, the information matrix for θ and a design ξ = (x1,…, x n ) is given by (4).
A k by k transition matrix ξ for the ancestral process of a single lineage should be considered and the Kronecker product of two of these transition matrices needs to be taken to obtain the fast-process matrix in the decomposition of the Markov chain for the ancestral process of two lineages.
Note that while we use the intra-class correlation matrix here, our theory does not depend on any specific structure for Ξ.
The effect of elasticity is dominant for ξ > 1while it is nearly negligible for ξ < 1.
for ξ ∈ H.
for ξ ∈ V.
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