A practical introduction to Bayes’ Theorem: Probability for Data Science Series (2)
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Bayes’ Theorem is one of the most widely used and celebrated concepts in statistics. It sets the basis of a probability theory that allows us to revise predictions or hypotheses based on new evidence.
In a previous article on probability notation, I introduced P(B∣A)— the probability of event B happening given that event A has already occurred.
Bayes’ Theorem flips this perspective, focusing on P(A∣B): the likelihood of A, given that B has occurred. In essence, it helps us refine our understanding of outcomes by incorporating prior information (known data).
In practice, even if your initial assumptions or estimates aren’t perfect, the process of applying the Bayes’ theorem encourages more thoughtful and informed guesses for the future!
To begin with, let’s look at an example inspired by the famous work of Daniel Kahneman and Amos Tversky.
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