Prior probability describes uncertainty before the current observations are incorporated. For a statistical parameter , a prior distribution assigns probabilities to its possible ranges, conditional on background information and a statistical model. A continuous prior density specifies these probabilities through integration:Here the probability density function is relative to Lebesgue measure ; a discrete prior distribution instead uses a probability mass function and sums. Density at one point is not the probability of that point. A nonnegative kernel with infinite total mass is an improper prior, whose use requires a separate check of posterior propriety.
For the coin model in Bayesian statistics, the beta distribution has prior density on . Its mean is and its variance is . It favors moderate heads probabilities without ruling out values near zero or one. For example,The prior curve in Bayesian posterior makes this preference visible; moment matching for a beta prior explains how to choose a beta distribution from a desired mean and variance.
A prior distribution lets earlier evidence or elicited knowledge contribute to statistical inference when the current sample size is limited. It also exposes assumptions that can be checked by prior elicitation and a prior predictive check. Before the same eight heads and two tails, a uniform prior would give posterior mean , compared with under : the observations do not uniquely determine a Bayesian posterior. Even a uniform prior makes a choice of parameter coordinates; its child article explains why changing coordinates can destroy uniformity.
Prior probability, often referred to simply as "prior," is a fundamental concept in Bayesian statistics. It represents the probability of an event or hypothesis before any new evidence or data is taken into account. In other words, the prior reflects what is known or believed about the event before observing any occurrences of it or collecting new data.
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