Posterior predictive checks are a Bayesian model assessment technique that involves comparing observed data to data simulated from the model's posterior distribution. This process helps evaluate how well the model captures the underlying structure of the data, allowing researchers to identify potential discrepancies between the model predictions and actual observations. By analyzing the similarities and differences between the simulated data and observed data, posterior predictive checks inform decision-making regarding model adequacy and selection.
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