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We deduce some well-posedness results.

Watson can deduce some information about a visitor to 221B Baker St Holmesseeseverythinging, and sees it instantly.

Then we deduce some useful corollaries.

We now deduce some special cases of Theorem 1.

We deduce some results from Theorem 2.1 as follows.

We immediately deduce some refinements for the identric mean I.

From their experience we can deduce some practical consequences.

To this aim, we denote h s ≜ μ s e j ϕ s s and h ̃ s = [ h s T, h s H ] T. We then deduce from assumptions A.1 and A.2 that the ML estimate of h ̃ s under H1 maximizes the Likelihood function L 1 ( h ̃ s ) given by L 1 ( h ̃ s ) = ∏ n = 0 K - 1 p [ b ̃ T v ( n T ) = x ̃ v ( n T ) - a n h ̃ s ∕ a n, R b ̃ ]. (D.1).

We deduce from assumptions A.1 and A.2 that the ML estimate of R b ̃ under H1, from primary and secondary observations, maximizes the Likelihood function L 1 ( R b ̃, μ s ϕ ̃ s ) = ∏ n = 0 K - 1 p [ b ̃ T v ( n T ) = x ̃ v ( n T ) - μ s a n S ϕ ̃ s / a n, S ] ∏ n = 0 K ′ - 1 p [ b ̃ T v ( n T ) = b ̃ T v ( n T ) ′ ]. (C.1).

Note that it is generally not possible to "work back", i.e. to deduce the assumptions underlying a given model formula.

For an ancestor sequence S0 of a strain S, we can deduce for the assumptions above that E(D S0, S)) ≈ K(T - T0), where D S0, S) is difference of the two sequences (as depicted by Hamming distance), T0 is the date of the last ancestor, T is the date of host death (as an estimate of sampling date), and K is the evolution rate constant.

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