Description of the method Sample Clauses

Description of the method. A. Path integral formalism
Sample 1
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Description of the method. The drum consists of a cylindrical part [internal diameter (i.d.) 16.3 cm, length 23.0 cm, volume
Sample 1
Description of the method. The project website was made using the Wordpress CMS, a popular, friendly and safe method to acknowledge a fast, flexible and powerful responsive website. This website is managed directly by IRTA Communication Team, that is responsible of provide regular updates with the partners information and with its dissemination activities. In the other hand, the i3-food intranet has been made using the Open Source technology “Owncloud”, that allow to create a safe intranet hosted on a chosen provider (In this case, “Webempresa”, a reliable hosting company with servers in Spain). This point and the flexibility to adapt the software makes it a better option that external services like Dropbox or similar (where sensible documents can be stored in countries outside the UE).
Sample 1
Description of the method m The maximum likelihood estimator ^θ= (β^1, . . . , ^βp, σ^2, . . . , σˆ2 )′ of θis the vector satisfying ^θ= argmaxθ∈Θ fθ(y) = argmaxθ∈Θ log fθ(y). Note that l(θ) = log fθ(y). We denote the vector of derivatives as S(θ) = (Sβ, Sσ2 , . . . , Sσ2 )′, where 0 m m ∂θ ∂β ∂σ2 ∂σ2 S(θ) = ∂l(θ) = ∂l(θ), ∂l(θ), . . . , ∂l(θ) ′ . 0 ^ If θ exists in the interior Θ, then it is the solution of the likelihood equations which are obtained by equating to zero the components of the vector of scores. By deriving the log-likelihood with respect to the parameters we obtain the score components of model (1.5), i.e. Sβ = X′V−1(y − Xβ), (1.8) i i S 1 ∂log |V|
Sample 1
Description of the method. Residual maximum likelihood estimation (REML) is introduced to reduce the bias of the maximum like- lihood estimators of the variance components. For this sake, it transforms the vector y in two independent vectors y⋆ = K1y and y⋆ = K2y, with the condition that the distribution of y⋆ does not depend on the
Sample 1
Description of the method. The maximum likelihood method gives at the same time the estimates of models coefficients β and components of variance σ2, . . . , σ2 . In this section we present the method of fitting constants to estimate
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