Posterior Distributions
In Bayesian statistics, posterior distributions are probability distributions that represent the updated beliefs about the parameters of a statistical model after taking into account new data. The posterior distribution is obtained by combining the prior distribution, which represents the initial beliefs about the parameters, with the likelihood function, which represents the probability of the observed data given the parameters. The resulting posterior distribution provides a complete summary of the uncertainty about the parameters, taking into account both the prior beliefs and the new data. Posterior distributions are important in Bayesian inference, which is a statistical approach that uses Bayes' theorem to update beliefs about the parameters of a model as new data becomes available.
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