prior probability

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  • Info 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. more...
  • Positive results are much more likely to be false if the prior probability of the claim under test is low.
  • Not taking prior probability into account partially or completely is called base rate neglect.
  • This approach is often used in giving estimates of prior probabilities.
  • It was unclear where these prior probabilities should come from.
  • The term is used to contrast the case of a weak or uniformative prior probability.
  • This probability estimate will often be used as the prior probability of a statement.
  • The prior probability of any statement is calculated from the number of bits needed to state it.
  • Generally the prior probability is estimated using the prevalence of a disease within a population or at a given testing location.
  • The idea of incorporating prior probabilities into an analysis has been suggested as a potential source of bias.
  • Among the criticisms of p-rep is the fact that it does not take prior probabilities into account.
  • This prior probability distribution might be based on our knowledge of frequencies in the larger population, or on frequency in the training set.
  • Bayesian learning methods make use of a prior probability that (usually) gives lower probability to more complex models.
  • He can test out his predictions (prior probabilities) through an experiment.
  • If shadow life actually exists, this would greatly increase the prior probability that extraterrestrial life has developed on other Earth-like planets.
  • In other words, for large n, the effect of the prior probability on the posterior is negligible.
  • The prior probability density distribution summarizes what is known about the distribution of r in the absence of any observation.
  • Classical probability can offer prior probabilities that reflect ignorance which often seems appropriate before an experiment is conducted.
  • Prior probabilities are the probabilities before a fact is known.
  • He can then assign to these events prior probabilities, which would be in the form of numerical weights.
  • Statistically, the prior probability distribution of x is sometimes taken to be a multivariate normal distribution.
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