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Bayes' theorem

Updated: 5/24/2026, 7:31:31 PM Wikipedia source

Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes (), gives a mathematical rule for inverting conditional probabilities, allowing the probability of a cause to be found given its effect. For example, with Bayes' theorem, the probability that a patient has a disease given that they tested positive for that disease can be found using the probability that the test yields a positive result when the disease is present. The theorem was developed in the 18th century by Bayes and independently by Pierre-Simon Laplace. One of Bayes' theorem's many applications is Bayesian inference, an approach to statistical inference, where it is used to invert the probability of observations given a model configuration (i ., the likelihood function) to obtain the probability of the model configuration given the observations (i ., the posterior probability).

Tables

· Examples › Medical diagnosis
Yes
Yes
SymptomCancer
Yes
Yes
1
No
0
Col 4
1
No
No
SymptomCancer
No
Yes
10
No
99989
Col 4
99999
Total
Total
SymptomCancer
Total
Yes
11
No
99989
Col 4
100000
SymptomCancer
Yes
No
Total
Yes
1
0
1
No
10
99989
99999
Total
11
99989
100000
Contingency table · Interpretations › Bayesian interpretations
A
A
Background Proposition
A
B
P ( B | A ) ⋅ P ( A ) {\displaystyle P(B|A)\cdot P(A)} = P ( A | B ) ⋅ P ( B ) {\d
⁠ ¬ B {\displaystyle \lnot B} ⁠ (not B)
P ( ¬ B | A ) ⋅ P ( A ) {\displaystyle P(\neg B|A)\cdot P(A)} = P ( A | ¬ B ) ⋅ P ( ¬
Total
⁠ P ( A ) {\displaystyle P(A)} ⁠
⁠ ¬ A {\displaystyle \neg A} ⁠ (not A)
⁠ ¬ A {\displaystyle \neg A} ⁠ (not A)
Background Proposition
⁠ ¬ A {\displaystyle \neg A} ⁠ (not A)
B
P ( B | ¬ A ) ⋅ P ( ¬ A ) {\displaystyle P(B|\neg A)\cdot P(\neg A)} = P ( ¬ A | B ) ⋅ P (
⁠ ¬ B {\displaystyle \lnot B} ⁠ (not B)
P ( ¬ B | ¬ A ) ⋅ P ( ¬ A ) {\displaystyle P(\neg B|\neg A)\cdot P(\neg A)} = P ( ¬ A | ¬ B )
Total
P ( ¬ A ) {\displaystyle P(\neg A)} = 1 − P ( A ) {\displaystyle 1-P(A)}
Total
Total
Background Proposition
Total
B
⁠ P ( B ) {\displaystyle P(B)} ⁠
⁠ ¬ B {\displaystyle \lnot B} ⁠ (not B)
P ( ¬ B ) = 1 − P ( B ) {\displaystyle P(\neg B)=1-P(B)}
Total
1
Background Proposition
B
⁠ ¬ ⁠ (not B)
Total
A
P ( B | A ) ⋅ P ( A ) {\displaystyle P(B|A)\cdot P(A)} = P ( A | B ) ⋅ P ( B ) {\d
P ( ¬ B | A ) ⋅ P ( A ) {\displaystyle P( eg B|A)\cdot P(A)} = P ( A | ¬ B ) ⋅ P ( ¬
⁠ P ( A ) {\displaystyle P(A)} ⁠
⁠ ¬ ⁠ (not A)
P ( B | ¬ A ) ⋅ P ( ¬ A ) {\displaystyle P(B| eg A)\cdot P( eg A)} = P ( ¬ A | B ) ⋅ P (
P ( ¬ B | ¬ A ) ⋅ P ( ¬ A ) {\displaystyle P( eg B| eg A)\cdot P( eg A)} = P ( ¬ A | ¬ B )
P ( ¬ A ) {\displaystyle P( eg A)} = 1 − P ( A ) {\displaystyle 1-P(A)}
Total
⁠ P ( B ) {\displaystyle P(B)} ⁠
P ( ¬ B ) = 1 − P ( B ) {\displaystyle P( eg B)=1-P(B)}
1

References

  1. Laplace refined Bayes's theorem over a period of decades: Laplace announced his independent discovery of Bayes' theorem
    http://gallica.bnf.fr/ark:/12148/bpt6k77596b/f32.image
  2. MacTutor History of Mathematics Archive
    https://mathshistory.st-andrews.ac.uk/Biographies/Price.html
  3. Liberty's Apostle
    https://www.uwp.co.uk/book/libertys-apostle-richard-price-his-life-and-times/
  4. David Hartley on Human Nature
    https://books.google.com/books?id=NCu6HhGlAB8C&pg=PA243
  5. Philosophical Transactions of the Royal Society of London
    https://doi.org/10.1098%2Frstl.1763.0053
  6. Notes and Records of the Royal Society of London
    https://doi.org/10.1098%2Frsnr.1968.0009
  7. Price: Political Writings
    https://books.google.com/books?id=xdH-gjy2vzUC&pg=PR23
  8. Mitchell 1911, p. 314.
  9. Classical Probability in the Enlightenment
    https://books.google.com/books?id=oq8XNbKyUewC&pg=PA268
  10. The History of Statistics: The Measurement of Uncertainty Before 1900
    https://books.google.com/books?id=M7yvkERHIIMC&pg=PA99
  11. Scientific Inference
    https://archive.org/details/scientificinfere0000jeff
  12. The American Statistician
    https://doi.org/10.1080%2F00031305.1983.10483122
  13. Stats, Data and Models
  14. The American Statistician
    https://doi.org/10.1080%2F00031305.1986.10475370
  15. Jahresbericht der Deutschen Mathematiker-Vereinigung
    https://doi.org/10.1365%2Fs13291-013-0069-z
  16. Significance
    https://doi.org/10.1111%2Fj.1740-9713.2013.00638.x
  17. The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines & Emerged Triumphant from Two Centuries of Controversy
    https://archive.org/details/theorythatwouldn0000mcgr
  18. Biometrika
    https://doi.org/10.1093%2Fbiomet%2F66.2.393
  19. Kendall's Advanced Theory of Statistics: Volume I – Distribution Theory
  20. Think Bayes: Bayesian Statistics Made Simple
    https://open.umn.edu/opentextbooks/textbooks/288
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