2.4 Posterior probability model

The posterior probability model is defined as: P(is fake | has !) and P(is real | has !)

and this can be calculated using Bayes’ Rule:

posterior=prior×likelihoodnormalizing constant

Fake Real
prior 40.0% 60.0%
likelihood 26.7% 2.2%
posterior 88.9% 11.1%

Shortcut to calculating the normalizing constant:

normalising constant=sum(prior×likelihood)