# prior probability

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## prior probability

n
(Statistics) statistics the probability assigned to a parameter or to an event in advance of any empirical evidence, often subjectively or on the assumption of the principle of indifference. Compare posterior probability
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A prior probability distribution is said to be conjugate to the sampling density if the resulting posterior distribution is a member of the same parametric family as the prior.
There are two cases to consider: The first is that B has beliefs about C's portfolio that are sufficiently well formed as to be described by a unique prior probability distribution, which means that for each portfolio that C could hold, B assigns a single probability number to the likelihood that C could hold that portfolio.
2] of the normal distribution are random variables with certain prior probability distribution.
At the time the attack is detected, we quantify our beliefs regarding the size of the attack by the prior probability distribution p(n) = Pr{N = n}.
From the prior probability distribution P(w i we can derive a new probability distribution P'' so that:
The precise posterior arrived at here is not the issue, since it can be manipulated at will by adjusting the prior probability distribution over the hypotheses.
Also, a prior probability distribution of the change-point and a joint density function of the sample mean and sample standard deviation have to be determined by the user in the light of the knowledge of a particular application.
In the normal regression case that we are concerned with, this prior probability distribution can be summarized by prior means and variances for the regression coefficients.
Assume a prior probability distribution of expected losses for a risk, relative to certain information.
It is beyond the scope of this editorial to describe the mathematical challenges when defining prior probability distributions and then combining those distributions with the likelihoods of new data.

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