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Bayesian classification, which includes Bayesian belief network (BBN) modeling, is a statistical method that represents conditional, probabilistic relationships between variables, or "features".
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For gene prediction, the MGA used di-codon frequencies that represent conditional probabilities of codon occurrences providing a previous codon (61 × 61 frequencies).
A Bayesian Network (BN) is a probabilistic graphical model that represents the conditional dependencies of a set of random variables with a directed acyclic graph (DAG).
where represents conditional likelihood, normalized between.
Parameter x represents conditional changes in the probability of choosing a specific transport mode related to infinitesimal variations in the specific constant and price.
In Eq. (4), ht(PSE) represents conditional variance of the Pakistan Stock Exchange, and parameter δ measures the effect of oil price shocks on the stock market of Pakistan.
A Bayesian network represents conditional dependencies between random variables with a directed acyclic graph.
Similarly, represents conditional dependence [ 26].
The structure represents conditional dependencies of classes on selected features.
This technique models and represents conditional probabilities in a graph.
Table 2 represents conditional functional breakdown of genes targeted by the NeuroStem microarray platform.
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