Given that the most computer intensive part of DISTMIX imputation is the reference panel-based computation of the correlation matrix, in future DISTMIX versions we plan to pre-compute, and store in a database, the local correlation matrix of genetic regions by each ethnic group (Pasaniuc et al., 2014).
The simulation-based computation of the RHS of (10), i.e., of the argument of the limit, is carried out based on a sequence of length n=106 symbols.
In order to sufficiently capture the information of human activity and be convenient for the FFT-based computation of the frequency-domain features, the window length is set to be 2 n.
Therefore, one obtains: Figure 14 Tree-based computation of the average delay D ¯ ( m ), when m = n + 2. D ¯ ( n + 2 ) = T src tx + ( D ¯ ( n + 1 ) + D ¯ i | i ) p Y ( 1 | S ) + ∑ i = 2 n D ¯ i | i p Y ( i | S ).
Therefore, the average delay D ¯ ( n + 1 ) can be obtained as follows: Figure 13 Tree-based computation of the average delay D ¯ ( m ), when m = n + 1. D ¯ ( n + 1 ) = T src tx + ∑ i = 1 n D ¯ i | i p Y ( i | S ).
In the presence of both aleatory and epistemic uncertainty, two challenges are addressed in this paper for the model-based computation of the Sobol indices: due to data uncertainty, input distributions are not precisely known; and due to model uncertainty, the model output is uncertain even for a fixed realization of the input.
In brief, the model-based computation of the activation sequence of a single beat can be described as follows: For an assumed model activation sequence the ECG is simulated and compared with the measured ECG.
We compared the runtime required by main memory and secondary memory-based implementations (Section "Secondary memory-based computation") of the RW algorithm described in Section "Local implementation of network-based algorithms".
By PLS-based computation of the intercepts, β0 and β1, we replaced log(I) with the resulting PLS-computed, mean-centered and scaled-to-unit variance residuals ε for further analysis.
Both are based on computation of the local control gains using Riccati design; the first is based on an H∞ type Riccati inequality and the second on an H2 type Riccati equation.
Two of these techniques use random sampling strategies [ 14], which are based upon computation of the topological distance between leaves in the source tree.
