We instead use conditional entropy constrained scalar quantizers (CEC-SQ), designed by modifying the algorithm in [27] to accommodate a conditional entropy constraint similar to CEC-TCQ approach above.
For practical implementation of SP-DTCs, the idea of using conditional entropy constrained (CEC) quantizers followed by Slepian-Wolf coding is explored.
In order practically implement this approach, we introduce the idea of designing conditional entropy constrained TCQ (CEC-TCQ) based on analytically found bit-allocations.
To overcome this limitation, we reformulate the problem of unconstrained optimization into a biologically justified constrained (conditional) optimization problem.
To estimate edge set E ^, we have to determine the node neighborhoods of all nodes in V. To achieve this goal, we solve a sparsity constrained conditional maximum likelihood estimation problem: (8) min U, W ∑ s ∈ V ℓ s (U, W ; D ) + α (Reg (U ) + Reg (W ) ).
This is tested using Wilks' theorem, where under the null hypothesis of conditional independence M1 is constrained to be independent of E2 at every level of M2.
This is a generalization of EM algorithm where the E step remains the same butthe M step is replaced by CM (constrained or conditional maximization) step.
Finally, we imposed measurement invariance (MI; constraining conditional health indicator probabilities to be the same across time points) to ensure health profiles have the same meaning at each occasion.
Partial correlations can be estimated assuming that the observations follow a graphical Gaussian model, in which (Sigma) is constrained by the conditional independence described by a graph (see e.g. [13]).
