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χ2 is the chi-square value for the given model with degrees of freedom, df, and probability, p. Δχ2 is the difference in chi-square between the given model and the previous one.
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Here, we derive EI of RW and RWJ for the model with degree correlation among neighbors as bivariate Pareto (7).
In section "Description of the configuration model with degree-degree correlation", the graph model and correlated graph generation technique are presented.
Furthermore, functions are developed to predict the evolution of AEVs, residual water content and fitting parameters of the van Genuchten model with degree of hydration.
The PageRank transition kernel (Eq. 11) on the degree state space does not depend upon the random graph model in section "Description of the configuration model with degree-degree correlation".
We find that in these cases, the marginal distribution of degrees of the random walk based methods also follow Pareto distribution (though we have derived only for the model with degree correlations among neighbors, see section "Degree correlations").
We presented the best model with degree 1 and penalty of 1.
However, the model with degree 4 shows a roughly 7% increase in A, when compared to lower polynomial degrees.
Otherwise, a polynomial regression model with degree 3 was used (both x and x 2 terms were included).
For B4405, the central points at positions 369 and 737 show larger fluctuations for models with polynomial degrees 3 and 4 than for the model with degree 2; see Figure 4.
Four different model networks with degrees of polymerization (DP) of the linear segments between cross-links of 10, 20, 50 and 100 were prepared.
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