Prior probability - Wikipedia, the free encyclopedia
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.
Probability that a certain event or outcome will occur. For example, economists may believe there is an 80% probability that the economy will grow by more than 2% in the coming year. Prior probability may be adjusted as new data becomes available.
http://www.investopedia.com/terms/p/prior_probability.asp
The probability that an event will reflect established beliefs about the event before the arrival of new evidence or information. Prior probabilities are the original probabilities of an outcome, which be will updated with new information to create posterior probabilities.
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.
Probability that a certain event or outcome will occur. For example, economists may believe there is an 80% probability that the economy will grow by more than 2% in the coming year. Prior probability may be adjusted as new data becomes available.
http://www.investopedia.com/terms/p/prior_probability.asp
The probability that an event will reflect established beliefs about the event before the arrival of new evidence or information. Prior probabilities are the original probabilities of an outcome, which be will updated with new information to create posterior probabilities.
Prior probabilities represent what we originally believed before new evidence is uncovered. New information is used to produce updated probabilities and is a more accurate measure of a potential outcome. For example, three acres of land have the labels A, B and C. One acre has reserves of oil below its surface, while the other two do not. The probability of oil being on acre C is one third, or 0.333. A drilling test is conducted on acre B, and the results indicate that no oil is present at the location. Since acres A and C are the only candidates for oil reserves, the prior probability of 0.333 becomes 0.5, as each acre has one out of two chances.
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