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Prior probability refers to the probability obtained based on past experience and analysis, such as the total probability formula, which is often used as the probability of the "cause" in the "cause-seeking" problem.
In Bayesian statistical inference, an uncertain number of prior probability distributions is a probability distribution that expresses a degree of confidence in this quantity before considering some factors. For example, a prior probability distribution may represent a probability distribution of relative proportions of voters voting for a particular politician in future elections. The unknown quantity can be a parameter of the model or a latent variable.
In Bayesian statistical inference, an uncertain number of prior probability distributions (often abbreviated as a priori) is a probability distribution that expresses a person's belief in that quantity before considering some evidence. For example, a priori can be a probability distribution that represents the relative proportion of voters who will vote for a particular politician in future elections. The unknown quantity can be a parameter or a potential variable of the model rather than an observable variable.
The Bayesian theorem calculates the renormalization point-by-point product likelihood function before the sum to produce a posterior probability distribution, which is a conditional distribution with a given amount of data uncertainty. Similarly, a priori probability of a random event or an uncertain proposition is an unconditional probability that any relevant evidence is taken into account before the allocation.