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In Bayesian inference, we can include uncertainty about one or more of the parameters defining the prior or likelihood function. Such uncertain parameters are called hyperparameters. The extra uncertainty introduced to the analysis through hyperparameters is integrated into the posterior distribution. The integration can be difficult for algebraic Bayesian analysis, but is very simple with Monte Carlo simulation, as explained in the turbine blade and pats examples





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