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Under such models, the variance of Y consists of two additive components, one representing the variance part due to the variability of θ and one due to the inherent variability of Y if θ did not vary, i.e., V(Y) = V(E(Y|θ)) + E(V(Y|θ)).
Probability for inter-individual variability of θ.
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The distribution of these average values over all re-sampled dataset for all the MD trajectories was subsequently employed to estimate the variability of the angle θ and distance d, and also to calculate variances and confidence intervals.
Consequently, inter-study variability of Θ t) is small, too.
Figure 1 Inter-study variability of AIF shape and Θ ( t ) = AUC ( t )/ c a ( t ) in the available 15 data sets.
The inferential solution to the inverse problem is based on the posterior distribution of θ, with the variability of ε ˜ and prior beliefs about θ conveyed through probabilistic models.
Our results highlight the importance of analyzing the spatial variability of δDθ, its influencing factors and θ in shallow soil layers separately.
The density of the conditional (given the variance parameters) posterior distribution of the elements of θ, according to Bayes' theorem, is given by p (θ | y, σ e 2, σ θ 2 ) = p (y | θ, σ e 2 ) p (θ | σ θ 2 ) p (y | σ e 2, σ θ 2 ) (5) The conditional modes can be obtained by maximizing Equation 5 over θ.
by definition of θ.
N θ denotes a neighborhood of θ.
θ, Watterson's estimator of θ per base pair.
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