# Prior Probability Value

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A Prior Probability Value is a probability value from a prior probability function.

**Example(s):****See:**Prior Probability Function, Posterior Probability.

## References

### 2011

- (Webb, 2011k) ⇒ Geoffrey I. Webb. (2011). “Prior Probability.” In: (Sammut & Webb, 2011) p.782
- QUOTE: 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.

- QUOTE: 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.

### 2003

- (Jaynes, 2003) ⇒ Edwin T. Jaynes. (2003). “Probability Theory: The Logic of Science." Cambridge University Press.