Posterior Distribution
In Bayesian statistics, the posterior 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 to obtain the posterior distribution. The posterior distribution represents the updated beliefs 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.
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