释义 |
Bayes' FormulaLet and be Sets. Conditional Probability requires that
 | (1) |
where denotes Intersection (``and''), and also that
 | (2) |
and
 | (3) |
Since (2) and (3) must be equal,
 | (4) |
From (2) and (3),
 | (5) |
Equating (5) with (2) gives
 | (6) |
so
 | (7) |
Now, let
 | (8) |
so is an event in and for , then
 | (9) |
 | (10) |
From (5), this becomes
 | (11) |
so
 | (12) |
See also Conditional Probability, Independent Statistics References
Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T. Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed. Cambridge, England: Cambridge University Press, p. 810, 1992.
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