Posterior Probability Distribution

In Bayesian statistics, the posterior probability distribution is the probability distribution of an unknown quantity, given a set of observed data. It is obtained by applying Bayes' theorem, which updates the prior probability distribution with the likelihood function of the observed data. The posterior distribution represents the updated belief about the unknown quantity after taking into account the observed data. It is a key concept in Bayesian inference and is used to make predictions, estimate parameters, and perform hypothesis testing. The shape of the posterior distribution depends on the prior distribution and the likelihood function, and it can be continuous or discrete.
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