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In communications theory, the amount of information sent from a source is calculated using a statistical entropy function.
It is calculated using a statistical entropy function but, contrary to communications theory, is expected to exist only in systems for which there is a non-arbitrary microstate/macrostate distinction.
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Several hundred candidate models were built using a correlative maximum entropy approach, and best-fitting models were selected based on statistical significance, predictive ability, and complexity.
The use of statistical entropy is justified by the fact that there was no exchange of matter at the initial time for such small isolated systems and the application of the molecular-kinetic theory is problematic.
Sequence conservation along alignments was visualized using an entropy plot as implemented into Bioedit [ 36].
Individual data partitions were tested for substitution saturation using a non-parametric statistical test based on an information entropy index [ 52] implemented in DAMBE 5.0.23 [ 53].
The second-stage algorithm uses a maximum entropy classifier.
Sparse data is handled using an entropy-based smoothing procedure to estimate robust substitution probabilities.
The posterior probabilities for the four classifiers are combined using an entropy-based weighting scheme.
The gene expression numbers are first discretized into several intervals using an entropy-based discretization method.
Thus, we are using a static and memoryless entropy coder.
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