A prior probability describes uncertainty about an event or parameter before the current observation is incorporated.
A prior density is the probability density function of a continuous prior distribution with respect to a specified reference measure. A proper prior density is nonnegative and integrates to one. Prior-density factors enter Bayesian posterior and RJ-MCMC acceptance formulas along with the likelihood and model prior probabilities.
A continuous spike-and-slab prior mixes a narrow continuous probability distribution around zero with a much wider component. It permits practical effect selection while retaining a continuous posterior density. Its narrow component is not the exact-zero mass of a point-null mixture prior. A latent component indicator aids computation; a wide-component draw can still have small magnitude, so posterior effect thresholds and component membership answer different questions.
A uniform prior on a finite-volume parameter region assigns constant probability density there and zero outside. On it is , giving a Beta distribution posterior after binomial distribution data. Uniformity depends on the coordinate: a nonlinear reparametrization introduces a Jacobian and generally does not preserve a uniform density.
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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.