Bayes - Medreonet

Step 3. Posterior distributions
The posterior distribution is the revised estimate
of the parameter we are investigating and is
obtained simply by multiplying the prior
distribution and the likelihood function
Since the individual probabilities calculated by the
likelihood function are independent of each other,
the resulting posterior probabilities need to be
normalised
This ensures that the area under the curve of a
continuous distribution equals one and that the
probabilities for a discrete distribution all add up
to one