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To derive SPE, SPE, and SPE, we need probability models for IC, ITD, and ILD.
To model independent phenotype selection events, we need probability distributions for the number of mutations in target genes (genes where mutations can cause the desired phenotype) and for non-target genes.
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We would need probabilities and some use of natural numbers too.
In order to determine the PRI, three characteristics are needed: probability (probability of the event occurring), severity (severity of the event), and detection (possibility of detecting critical aspects or identifying the failure through controls before the event has produced its negative effects) [ 9].
The needed probabilities on the right side of Equation 2 can easily be estimated from the training data (using Laplace smoothing to account for zero counts).
During the computation we accumulate the needed probabilities in the arrays T e m p R P r o b w, q), see section C of the algorithm 2, lines 15 26.
We need three probability distributions to calculate the mutual information.
While this method does not need assumed probability distribution for uncertainties, it needs to carefully select the uncertainty intervals.
In this paper, we use the non-probabilistic convex model to deal with the uncertain parameters in which there is no need for probability density functions.
Most previous methods, such as the expected value and expected residual minimization, need the probability distribution information of the stochastic variables.
c) Robust optimization Robust optimization uses intervals to model uncertain variables and thus does not need accurate probability distribution functions and fuzzy membership functions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com