In Bayesian statistical inference, a prior probability distribution, often called simply the prior, of an uncertain quantity p is the probability distribution that would express one’s uncertainty about p before some evidence is taken into account. For example, p could be the proportion of voters who will vote for a particular politician in a future election. It is meant to attribute uncertainty, rather than randomness, to the uncertain quantity. The unknown quantity may be a parameter or latent variable. One applies Bayes’ theorem, multiplying the prior by the likelihood function and then normalizing, to get the posterior probability distribution, which is the conditional distribution of the uncertain quantity, given the data. A prior is often the purely subjective assessment of an experienced expert. Some will choose a conjugate prior when they can, to make calculation of the posterior distribution easier. Parameters of prior distributions are called hyperparameters, to distinguish them from parameters of the model of the underlying data. … Prior Probability

# If you did not already know: “Prior Probability”

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