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First, it is difficult to ensure that each column is discrete with only one value.
Suppose now that it is discrete with possible outcomes, which is the usual case in the filter bank context.
The classic maximum likelihood based derivation of the product-limit estimator starts by assuming the underlying distribution is discrete with probabilities π j =P(X=x (j)), j=1,2,⋯,g for g≤n.
Then, by the similar proof to that of Theorem 2, we obtain (i) For every ε>0, the set mathbb Y^{epsilon}=left{Y^{epsilon}_{u}: u in {0, 1}^{mathbb{Z}_{>0}}right} /text{isometry} is discrete with cardinality of the continuum.
The proof of this proposition is based on convex optimization tools and is given in Appendix C. The intuition is that, at the convergence state, the iteration, which is discrete with respect to the spectrum allocation policy by construction, satisfies the KKT conditions which are both necessary and sufficient for optimality in convex optimization problems.
A classical RBN is a dynamical system, based on a directed graph with N nodes (genes), which can assume binary values 0 or 1 (inactive/active); time is discrete, with synchronous updating of all the node values.
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Transitions among different phases were discrete with hysteresis loops.
All nodes are discrete, with mutually exclusive and exhaustive states (Figure 3).
Clinically the disease may be discrete, with conductive hearing loss, otorrhea and history of chronic otitis.
Because the data are discrete with time intervals, selecting a partial segment is unnecessary.
The given results show that the minimum mass complexity of the optimized bridge model has a multiscale character, being discrete with respect to the first complexity parameter, and markedly or infinitely large with respect to the second complexity.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com