Prior Probabilities

A prior probability is a marginal probability, interpreted as a description of what is known about a variable in the absence of some evidence. The posterior probability is then the conditional probability of the variable taking the evidence into account. The posterior probability is computed from the prior and the likelihood function via Bayes' theorem.
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Proportionate distribution of classes in the population (in a classification problem), especially where known to be different than the distribution in the training data set. Used to modify probabilistic neural network training in neural networks .
See also, Neural Networks , Discriminant Function Analysis .