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See also: Bayesian probability, Priors, Likelihood ratio, Belief update, Probability and statistics, Epistemology, Bayesianism
See also: Bayesian probability, Priors, Likelihood ratio, Belief update, Probability and statistics, Epistemology, Bayesianism
Bayes' Theorem (also known as Bayes' Law) is a law of probability that describes the proper way to incorporate new evidence into prior probabilities to form an updated probability estimate. It is commonly regarded as the foundation of consistent rational reasoning under uncertainty. Bayes Theorem is named after Reverend Thomas Bayes who proved the theorem in 1763.
See also: Bayesian probability, Priors, Prior probability, Likelihood ratio, Posterior probability, Belief update, Probability and statistics, Epistemology
\[{\displaystyle {\frac {P(A|B)}{P(\neg A|B)}}={\frac {P(A)}{P(\neg A)}}\cdot {\frac {P(B|A)}{P(B|\neg A)}}}\]
Bayes'Bayes' Theorem (also known asBayes'Bayes' Law) is a law of probability that describes the proper way to incorporate new evidence into prior probabilities to form an updated probability estimate. It is commonly regarded as the foundation of consistent rational reasoning under uncertainty. Bayes Theorem is named after Reverend Thomas Bayes who proved the theorem in 1763.See also:Bayesian probability,Priors,Likelihood ratio,Belief update,Probability and statistics,Epistemology,BayesianismBayes'Bayes' theorem commonly takes the form:P(A|B)=P(B|A)P(A)P(B)¬¬A|B)=P(A)P(¬¬A)⋅⋅P(B|A)P(B|¬¬A)VisualizationVisualisation ofBayes'Bayes' RuleExternal linksArbital Guide to Bayes' RuleAn Intuitive Explanation of Bayes' Theoremby Eliezer YudkowskyVisualizing Bayes' theoremby Oscar BonillaUsing Venn pies to illustrate Bayes' theorembyoracleaideA Guide to Bayes’ Theorem – A few linksby Alexander KruelBayes' Theorem, Wikipedia