Bayes’ Theorem in AI
In probability theory, Bayes’ theorem talks about the relation of the conditional probability of two random events and their marginal probability. In short, it provides a way to calculate the value of P(B|A) by using the knowledge of P(A|B).
Bayes’ theorem is the name given to the formula used to calculate conditional probability. The formula is as follows:
[Tex]P(A∣B)=P(A∩B)/P(B)=(P(A)∗P(B∣A))/P(B)[/Tex]
where,
- P(A) is the probability that event A occurs.
- P(B) defines the probability that event B occurs.
- P(A|B) is the probability of the occurrence of event A given that event B has already occurred.
- P(B∣A) can now be read as: Probability of event B occurring given that event A occurred.
- p(A∩B) is the probability events A and B will happen together.
Bayes’ theorem in Artificial intelligence
The Bayes Theorem in AI is perhaps the most fundamental basis for probability and statistics, more popularly known as Bayes’ rule or Bayes’ law. It allows us to revise our assumptions or the probability that an event will occur, given new information or evidence. In this article, we will see how the Bayes theorem is used in AI.
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