A posterior predictive check is a technique used in Bayesian statistics to evaluate the fit of a model by comparing observed data with data simulated from the posterior predictive distribution. This method helps assess how well a model can replicate the observed data and identify areas where the model may not adequately capture the underlying patterns in the data. By generating new data points based on the posterior distribution of the parameters, this technique allows for a more intuitive understanding of model performance.
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