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What are the main components of a Bayesian Model?

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Adapted from Bayes’ Rule, the basic setup of Bayesian inference is:

where is the posterior, or the distribution of the parameter updated after observing data X. is the likelihood of the observed data

is the prior distribution assigned to based on a subjective degree of beliefP(X) is the marginal distribution of X that normalizes the posterior into a valid probability distribution

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