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We hereby describe the AIC algorithm in a matrix formulation.
This paper presents a power series method with domain partition implemented in a matrix formulation, as an alternative to other power series techniques in vibration analysis.
This equation can be rewritten in a matrix formulation: (5) Err Z − C D X = Z − C D C D T C D − 1 C D T Z.
Thus, the model can be rewritten in a matrix formulation: (3) Z C D X, X = C D T C D − 1 C D T Z, where Z = (g k ); X = (x i ) and C is the marking matrix recording whether the DHS sites are within the transcriptional regulatory regions of differentially expressed genes or not.
Eq. 1 can be rewritten in a matrix formulation: G ¯ = T X ¯ − M Y ¯ = [ T M ] [ X ¯ − Y ¯ ] where T = (t k,i) and M = (m k,j); If we define N = [ T, M] and A = [ X T, - Y T]T, the X and Y can then be estimated: A ^ ¯ = (N T N ) − 1 N T G ¯ The first n t and the last n m elements in A vector can be referred as the estimation for X and Y, respectively.
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In addition, a matrix formulation to determine mode coefficients of scattered waves were used.
In equation (4) we used a matrix formulation, which is shorthand for the optimization problem in terms of all q= r + s + t binary EM vectors e i, max | | x | | s.t. e g T x = | | e g | |, g ∈ { 1, …, r } e h T x ≤ | | e h | |, h ∈ { r + 1, …, r + s } e k T x ≤ | | e k | | − 1, k ∈ { r + s + 1, …, r + s + t }.
These links are conveniently defined through a matrix formulation in which system elements from one component are mapped onto system elements from another component.
In many applications of Bernoulli polynomials, a matrix formulation for the Bernoulli polynomials seems to be useful.
These genotype probabilities were then used in maximum likelihood parentage analysis, using a matrix formulation to simplify the algebra and reduce the risk of software errors.
In order to evaluate this alternative, the effectiveness of VI as a tool in porous matrix formulation is analyzed.
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