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Gibbs Sampling is a Markov Chain Monte Carlo algorithm used to generate samples from a multivariate probability distribution when direct sampling is difficult. It iteratively samples each variable from its conditional distribution, given the current values of the other variables, allowing for efficient approximation of complex distributions.
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