We interpolated the intermediate values from 0 bp to 15 bp between binding sites and gave a maximum weight of 8.27 to dimers 6 bp apart.
In this setting, seemingly high values from small windows may be less informative than seemingly intermediate values from large windows, due to the greater sampling error associated with fewer markers being included in smaller windows.
A proof of that fact requires the intermediate value theorem from elementary calculus.
Haplotype AI had intermediate values for these parameters and was not different from either haplotype M or I2 and M1.
(2) ⇒ (3): This follows directly from the intermediate value theorem.
Then, from the intermediate value theorem it follows that there exists a critical value (hat{gamma }^{R} in (0,1)) such that (Delta U^{ R}left( {hat{gamma }^{R} } right) = 0).
Then, from the intermediate value theorem, there exists a constant μ ∗ ∈ ( 1, β + 1 ) such that f = 0. Therefore, we can obtain μ ¯ = r ( A ) > 1.
It follows from the intermediate value theorem [11] that there exists a μ ∗ ∈ such that L = 0, that is y ( 1 ) = y μ ∗ ( 1 ) = 0, which satisfies the second boundary value condition of (1.2).
It follows from the intermediate value theorem [21] that there exists μ ∗ ∈ such that L = 0, that is, y ( 1 ) = y μ ∗ ( 1 ) = 0, which satisfies the second boundary value condition of (1.2).
When k s >k opt, there exists k 0∈[ 0,k opt] such that Γ k 0 =Γ k s ) from the intermediate value theorem, since Γ k)≥0 is continuous and 0≤Γ k s )≤Γ k opt).
