Prior predictive checks are a method used in Bayesian statistics to evaluate the plausibility of prior distributions by comparing simulated data generated from the prior to observed data. This technique helps in assessing whether the chosen priors are reasonable before analyzing actual data. By generating predictions based on the prior, researchers can visually and statistically compare these predictions to real-world observations, ensuring that their prior beliefs are coherent and relevant.
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