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Exact(6)
Let θ=⊤ be the parameter vector.
Let x 1⋯,x n be a random sample from (5) and θ=(c,α,β,s) T be the parameter vector.
Let u j (t) be the parameter vector for the single offspring of the j th parent in the t th generation.
Let Θ=[θ1,…,θ K ]T be the parameter vector for our logistic regression model such that (3) and logistic(x)=(1+ e− x )−1 is the standard mapping from ℝ→[0, 1].
Let θ = (H, σ 1, σ 2, β 1, β 2, α ) be the parameter vector that needs to be optimized (these peak parameters are introduced in the next sub-section).
For an observation i, the full likelihood for vQTL with genotype uncertainty is L i = ∑ j = 1 3 p i j N (μ + x i T β + r j α, σ 2 exp (z i T γ + r j θ ) ) = ∑ j = 1 3 p i j f i j with variables other than f ij defined as for Equation 3. Let l i = log(L i), g ij = log(f ij), and ψ be the parameter vector, and assume observations are independent given the parameters such that L = ∑ i n L i.
Similar(54)
In the consequent, a i is the parameter vector and b i is the scalar offset.
θ i, i = 1, …, N is the parameter vector of distribution.
A N = [a 0, a 1, a 2, ⋯, a n ] T is the parameter vector of the polynomial model.
where X iq is explanatory variable matrix of V iq and β' is the parameter vector associated with the matrix X iq.
Draw n random observations from a probability distribution f X (x|θ), where θ is the parameter vector (see Table 1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com