Posterior predictive checks are a Bayesian model evaluation technique that compares observed data to data simulated from the posterior distribution of a model. This method provides insight into how well the model predicts new data and whether it captures the underlying structure of the observed data. By assessing discrepancies between actual and predicted values, researchers can identify potential weaknesses in their models and improve them accordingly.
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