Bayes' theorem# GeoSpace - 地球与空间科学
p*t
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Bayes' theorem relates the conditional and marginal probabilities of
stochastic events A and B:
Pr(A|B) = {Pr(B|A)×Pr(A)}/{Pr(B)} = {L(A|B)×Pr(A)}/{Pr(B)}
where
L(A|B) = \Pr(B|A)
is the likelihood of A given B for a fixed value of B.
Each term in Bayes' theorem has a conventional name:
* Pr(A) is the prior probability or marginal probability of A. It is "
prior" in the sense that it does not take into account any information about B
.
* Pr(A|B) is the conditional probability of A, gi
stochastic events A and B:
Pr(A|B) = {Pr(B|A)×Pr(A)}/{Pr(B)} = {L(A|B)×Pr(A)}/{Pr(B)}
where
L(A|B) = \Pr(B|A)
is the likelihood of A given B for a fixed value of B.
Each term in Bayes' theorem has a conventional name:
* Pr(A) is the prior probability or marginal probability of A. It is "
prior" in the sense that it does not take into account any information about B
.
* Pr(A|B) is the conditional probability of A, gi
