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MutPred (Li et al., 2009) evaluates the probabilities of gain or loss of structure and function upon mutations and predicts their impact using a Random Forest-based approach.
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The GHK simulator evaluates the probability each individual contributes to the likelihood function.
A MAP detector accepts the received vector y and the a priori information, coming from the decoder, and evaluates the probability of all possible transmitted vectors x.
The assignment submodel evaluates the probability that an individual is assigned to a particular cluster.
GeneSV evaluates the probability of selected mutation(s) first by analyzing its (their) frequency (frequencies) as observed in the genomic sequence databases.
PEBL evaluates the probability of a discretized dataset given a topology using the BD (Bayesian Dirichlet) scoring metric described elsewhere[ 14].
For this purpose we applied a statistical framework that evaluates the probability of each transcription factor to be responsible for the observed differential expression pattern.
They proposed an algorithm that separately evaluates the probability of each possible combination of line origins between fully informative markers, then sums and normalizes the results.
The used model made it possible to evaluate the probabilities of particle escape into the bulk and their rearrangement on the surface.
We evaluated the probabilities to implement possible functions for each motif and found that the kurtosis of these distributions correlate well with the natural abundance pattern.
The main advantage over the exact likelihood is that we only need to evaluate the probabilities for family configurations that are both observed and ascertained.
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