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Bayesian Statistics is centered around one major theorem named Bayes' Theorem. This theorem is used to update probabilities once new data is introduced. | Bayesian Statistics is centered around one major theorem named Bayes' Theorem. This theorem is used to update probabilities once new data is introduced. | ||
| − | Bayes' Theorem: <math> P(A | B) = \frac{P(B | A)P(A)}{P(B)}</math> | + | Bayes' Theorem is stated as: <math> P(A | B) = \frac{P(B | A)P(A)}{P(B)}</math> where A usually represents the proposition of the statement and B represents the evidence or new data. |
Revision as of 21:57, 6 December 2020
Bayesian Statistics
What is Bayesian Statistics?
Bayesian statistics is a theory based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. This degree of belief might be based on prior knowledge of the event or on personal beliefs on the event. These methods use Bayes’ theorem to calculate and renew probabilities after obtaining new data. The theorem describes the conditional probability (probability of one event occurring with some relationship to one or more other events) based on data and prior beliefs or conditions related to the event.
Bayesian Statistics is centered around one major theorem named Bayes' Theorem. This theorem is used to update probabilities once new data is introduced. Bayes' Theorem is stated as: $ P(A | B) = \frac{P(B | A)P(A)}{P(B)} $ where A usually represents the proposition of the statement and B represents the evidence or new data.
