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for i = 1, 2, …, N. Since T i is L i -Lipschitz continuous, we have that ∥ T i x n − T i y n i ∥ ≤ L i ∥ x n − y n i ∥ (2.11).
I'm L-Train, man.
I'm L-Train.
H: Yeah I was! L: I'm gonna ask your Mama.
I am l.
After incorporating a discrete Γ model, the likelihood of observing the pattern of gene family i will be L + i = ∑ j = 1 M p j L i (μ j ) 1 − L − i (μ j ).
Let N i i ⩽ L be L independent Nakagami-m RVs of average energies 2 σ i 2, and the same fading parameter m.
Thus if the likelihood on gene family i with rate μ is L i, and the density function of the distribution of rates is f, the likelihood on site i will be L i = ∫ 0 ∞ f L i d μ = ∑ j = 1 M p j L i (μ j ).
Assume that each T i is uniformly L i -Lipschitz continuous with μ 1 ( i ) = 0 for each i ≥ 1.
Moreover, by the assumption that ∀i ≥ 1, S i is uniformly L i -Lipschitz continuous, and hence we have.
We use the assumptions that for each i ≥ 1, T i is uniformly L i -Lipschitz continuous.
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