A sensible introduction to Bayes’ Theorem: Likelihood for Knowledge Science Sequence (2)
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Bayes’ Theorem is among the most generally used and celebrated ideas in statistics. It units the idea of a likelihood principle that enables us to revise predictions or hypotheses primarily based on new proof.
In a earlier article on likelihood notation, I launched P(B∣A)— the likelihood of occasion B taking place on condition that occasion A has already occurred.
Bayes’ Theorem flips this angle, specializing in P(A∣B): the probability of A, on condition that B has occurred. In essence, it helps us refine our understanding of outcomes by incorporating prior data (recognized information).
In observe, even when your preliminary assumptions or estimates aren’t good, the method of making use of the Bayes’ theorem encourages extra considerate and knowledgeable guesses for the long run!
To start with, let’s have a look at an instance impressed by the well-known work of Daniel Kahneman and Amos Tversky.