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In Bayesian inference, a prior probability of a value x of a random variable X, P(X = x), is the probability of X assuming the value x in the absence of (or before obtaining) any additional information. It contrasts with the posterior probability, P(X = x | Y = y), the probability of X assuming the value x in the context of Y = y.
For example, it may be that the prevalence of a particular form of cancer, exoma, in the population is 0.1%, so the prior probability of exoma, P(exoma = true), is 0.001. However, assume 50% of people who have skin discolorations of greater than 1 cm width (sd > 1cm) have exoma. It follows that the posterior probability of exoma given sd > 1cm, P(exoma = true | sd > 1cm = true), is 0.500.
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© 2011 Springer Science+Business Media, LLC
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Webb, G.I. (2011). Prior Probability. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_658
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DOI: https://doi.org/10.1007/978-0-387-30164-8_658
Publisher Name: Springer, Boston, MA
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