Homonym: priors (Legal)
In statistics and Bayesian inference, "priors" refer to prior beliefs or knowledge about a parameter before observing any data. These beliefs are expressed mathematically as a probability distribution, which helps to incorporate existing information into the analysis. For example, if you are estimating the average height of a group, your prior might reflect what you already know about average heights in similar populations.
When new data is collected, the prior is updated to form a "posterior" distribution, which combines the prior information with the new evidence. This process allows for a more informed estimate of the parameter. The choice of prior can significantly influence the results, making it essential to select them carefully based on relevant data and context.