This approach greatly simplifies our gain matrix calculation (Eq. (3)). For simplicity, we assume a fixed uncertainty of 40% for coefficients corresponding to a priori CO2 fluxes over each sub-region, and a lar
Each feasible adjacency matrix's probabil- ity mass is proportional to the product of its edge weights. For example, if the optimiza- tion problem is the maximum-weight k-edge connected subgraph problem, the distribution assigns a non-zero probability mass to all adjacency matrices of graphs ...
Colors of different patterns are consistent with previous and following figures where the same example session is analyzed. (B) Pearson correlation matrix between patterns reveals significantly larger overlaps in the empirical data (top left: representative session) compared with those found when drawing...
See Fig. 3 for a correlation matrix of all word properties examined. Results were rela- tively consistent between the analyses based on all available words and the 1,185 subset, as shown in Table 1. Some lexical dimensions also performed well in explaining recall, particularly word frequency,...
the application of the normative model yielded a 990 × 400z-score deviation matrix,Z. Next, we used 10-fold cross-validation to also generate deviations in the training subset. The 281 individuals in the training subset were split into 10 folds, wherein 90% of the subset were used ...
Panel B. Pearson's pairwise correlation matrix Empty CellReturnSTVBetaSizeMomRevIlliqLt_revVolIvolMaxMinPTVVolumeStdVolumeSkew1Skew2IskewCoskew STV −0.08 Beta 0.00 −0.03 Size −0.02 0.00 −0.04 Mom 0.00 0.19 −0.01 0.06 Rev −0.26 0.18 0.00 0.05 −0.23 Illiq 0.01 −0.01...
SHAP interaction values are a generalization of SHAP values to higher order interactions. Fast exact computation of pairwise interactions are implemented for tree models withshap.TreeExplainer(model).shap_interaction_values(X). This returns a matrix for every prediction, where the main effects are on...
SHAP interaction values are a generalization of SHAP values to higher order interactions. Fast exact computation of pairwise interactions are implemented for tree models withshap.TreeExplainer(model).shap_interaction_values(X). This returns a matrix for every prediction, where the main effects are on...
; B-P test is the Breusch- Pagan test for diagonal covariance matrix ((χ2 45 d.f.); SSE is the sum of squared errors; GCV is generalised cross-validation, and D-W is the Durbin-Watson statistics. J-B is the Jarque Bera test for normality. (*), (**) and (***) indicate ...